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<head>
	<title>Cartesian Genetic Programming for the Java Evolutionary Computing Toolkit (CGP for ECJ)</title>
</head>

<body>
	
<div class="toptitle">Cartesian Genetic Programming for the Java Evolutionary Computing Toolkit (CGP for ECJ)</div>
<h1>Table of Contents</h1>
<ul>
	<li><a href="#intro">Introduction</a></li>
	<li><a href="#cgp">Cartesian Genetic Programming</a></li>
	<li><a href="#run">Running CGP for ECJ</a></li>
	<li>
		<a href="#problems">Sample problems</a>
		<ul>
			<li><a href="#reg">Regression</a></li>
			<li><a href="#class">Classification</a></li>
			<li><a href="#parity">Parity</a></li>
		</ul>
	</li>
	<li><a href="doc/index.html">Appendix: Class documentation</a>
	<li><a href="#ref">References</a></li>
</ul>

<a name="intro"></a><h1>Introduction</h1>

<p>This document describes a Cartesian Genetic Programming (CGP) implementation for ECJ, the Java Evolutionary Computation Toolkit.  CGP is a form of Genetic Programming invented by <a href="http://www.cartesiangp.co.uk/">Julian Miller</a><sup><a href="#r1">[1]</a></sup>.  In CGP, computer programs are encoded as integer or real-valued vectors instead of tree structures.  The vectors encode feed-forward graph representations of computer programs.  Evolutionary search is performed on the space of these computer programs to find solutions of interest.  CGP representations of computer programs have the following advantageous properties:</p>

	<ul>
		<li>Compactness of representation simplifies many genetic operations.</li>
		<li>Fixed-length genome enforces upper bound on program size.  This combined with other factors helps prevent program bloat.<sup><a href="#r3">[3]</a></sup></li>
		<li>CGP has a form of redundancy involving inactive program nodes that have been shown to be very beneficial to evolutionary search.<sup><a href="#r4">[4]</a></sup></li>
	</ul>
	
<p>
	CGP for ECJ supports integer and real-valued representations for the genomes.  The real-valued genomes allow us to use the new crossover operator that greatly enhances convergence performance for real-valued regression problems<sup><a href="#r2">[2]</a></sup>.
</p>

<p>
	This distribution contains everything you need to start building and running your own CGPs, and includes some sample regression, parity, and classification problems.  To compile and run CGP for ECJ, you will need to use Java 1.5 or higher.
</p>

<a name="cgp"></a><h1>Cartesian Genetic Programming</h1>

<h2>Representation</h2>
<h3>Integer-valued</h3>
<p>A Cartesian Genetic Program is represented by a vector of integer values which encode the structure of an acyclic graph.  The graph is associated with some number of input nodes, output nodes, and function nodes.  Each function node of the graph represents a function that is given some number of inputs.  Inputs for function nodes come from other function nodes, or from terminal (input) nodes.  Each output node is directly connected to one of the function nodes.</p>

<img src="fig1.jpg">
<div class="caption">Figure 1: Example 3x4 integer genome representation.<sup><a href="#r1">[1]</a></sup>.  The vector at the top is the genotype.  The grid of numbered nodes is the phenotype.</div>

<p><i>Figure 1</i> demonstrates the encoding of a sample integer vector.  Inputs are numbered from 0 to 5.  Outputs are labelled 13, 16, and 17.  Each function node is shown as a numbered box with numbered inputs entering on the left and an output node showing the node number on the right.  The number in the box indicates the function used by the function node.  For example, "0" maybe used to represent "Addition", "1" to represent "Subtraction", and "2" to represent "Multiplication."  Output 13 is evaluated by computing the value of function node 13, which evaluates function 1 on the inputs 9, 11, and 10.  Each of these inputs is a function node which is also evaluated with its inputs.  Evaluation continues in this way until terminal (input) nodes are reached.  Grayed-out areas in the figure represent nodes that are inactive.  These nodes are inactive because they are not connected to any output or any nodes involved in the evaluation of the outputs.  Inactive nodes play a key role in evolutionary search because they help explore the search space as a result of normal genetic operations.<sup><a href="#r2">[4]</a></sup>  Inactive nodes impose no additional computation burden until they become active by becoming connected to one of the paths from output nodes to terminal nodes.  
</p>

<p>
	The CGP representational scheme enforces a feed-forward, acyclic network of function evaluations by constraining the range of values allowed for arguments of each function node.  A function node's arguments must refer either to terminal (inputs) nodes, or to function nodes that are positioned to the left of the function node.  
</p>

<p>
	CGP for ECJ uses a one-dimensional (single row) grid of function nodes.  This convenient single row representation has been shown to be no less effective than the multi-row representation given in the CGP literature.
</p>

<img src="fig2.jpg">
<div class="caption">Figure 2: Example one-row integer genotype and corresponding phenotype.<sup><a href="#r2">[2]</a></sup>.  The vector at the top is the genotype.  Below it is the phenotype, an evaluated program that implements the function <i>x<sup>6</sup> - 2x<sup>4</sup> + x<sup>2</sup></i>.  Inactive nodes are grayed out in both the genotype and phenotype.</div>  

<h3>Real-valued</h3>
<p>
	CGP for ECJ also supports real-valued encoding of the one-row genotype.  In this encoding, each position in the genotype is allowed to be a real value from the range [0, 1].  During the evaluation process, this real-valued encoding is converted to the integer-valued encoding described above.  The motivation for this encoding is to introduce a real-valued crossover operator that improves convergence performance on certain evolutionary search problems<sup><a href="#r2">[2]</a></sup>.  Prior work has found that traditional crossover operators have reduced the performance of evolutionary search in both CGP and GP.  Thus, many experimenters choose to leave crossover operators out of their experiments.  Clegg et al [2007] found that their new real-valued crossover operator significantly improves performance for symbolic regression problems in CGP.  These results are reproduced by the sample regression problems included in this CGP package for ECJ.  However, I have not yet found similar improvements by using the same real-valued encoding and new crossover operator for the parity and classification problems included in this package.  In fact, using the same approach for these problems is detrimental to their performance.  Discovering improved genetic operators for these problems remains an open area of research.
</p>

<img src="fig3.jpg">
<div class="caption">Figure 3: Example one-row real-valued genotype and corresponding mapping to integer-valued genotype.<sup><a href="#r2">[2]</a></sup>.</div>  

<a name="run"></a><h1>Running CGP for ECJ</h1>

<p> 
	Included in this distribution is a collection of shell scripts that run CGP for ECJ using a number of sample parameter files that implement the included <a href="#problems">sample problems</a>.  The following is a list of these scripts:
	<tt> 
		run-breast-w.float.1+4.sh<br/>
		run-breast-w.float.pop50.sh<br/>
		run-breast-y.float.1+4.sh<br/>
		run-breast-y.float.pop50.sh<br/>
		run-iris.float.1+4.sh<br/>
		run-iris.float.pop50.sh<br/>
		run-parity-even-2.float.1+4.sh<br/>
		run-parity-even-2.float.pop50.convex.sh<br/>
		run-parity-even-2.float.pop50.sh<br/>
		run-parity-even-2.int.1+4.sh<br/>
		run-parity-even-3.float.1+4.sh<br/>
		run-parity-even-3.float.pop50.sh<br/>
		run-parity-even-3.int.1+4.sh<br/>
		run-parity-even-4.float.1+4.sh<br/>
		run-parity-even-4.float.pop50.sh<br/>
		run-parity-even-4.int.1+4.sh<br/>
		run-parity-even-5.float.1+4.sh<br/>
		run-parity-even-5.float.pop50.sh<br/>
		run-parity-even-5.int.1+4.sh<br/>
		run-parity-even-6.float.1+4.sh<br/>
		run-parity-even-6.float.pop50.sh<br/>
		run-parity-even-6.int.1+4.sh<br/>
		run-parity-even-7.float.1+4.sh<br/>
		run-parity-even-7.float.pop50.sh<br/>
		run-parity-even-7.int.1+4.sh<br/>
		run-parity-even-8.float.1+4.sh<br/>
		run-parity-even-8.float.pop50.sh<br/>
		run-parity-even-8.int.1+4.sh<br/>
		run-regression-1.float.pop50.sh<br/>
		run-regression-1.int.1+4.sh<br/>
		run-regression-2.float.pop50.sh<br/>
		run-regression-2.int.1+4.sh<br/>
		run-regression-3.float.pop50.sh<br/>
		run-regression-3.int.1+4.sh
	</tt>
</p>
<p>This distribution contains all of the needed binaries, so you can run any of these scripts right away to test drive CGP for ECJ, provided that you have Java 1.5 or above in your path.  ECJ version 18 is included as a jar file in the <b>lib</b> directory for your convenience.  Please note that I have not tested CGP for ECJ against earlier versions of ECJ.  Refer to the <a href="#problems">sample problems</a> section for more information about the included sample problems.</p>
<p>
	A good way to see how CGP for ECJ is configured is to look at the comments in the sample parameter files included in this distribution.  Here are some steps to get you started in setting up your own CGP experiments:
</p>

<h3>Choose algorithm</h3>

<p>The sample parameter files implement two different algorithms.  The first is a basic GA that uses tournament selection to select parents from a small population for breeding.  New children are made using mutation.  In the regression experiments using the real-valued representation, crossover is also used.  The second algorithm is a mu+lambda evolutionary strategy which configures a single parent to breed four children using mutation at each generation.  The best individual among the parent and children is kept in the next generation and the process is repeated.  Customized extensions to the ES selection operator and Mu/Lambda breeder are used to exploit the neutral search that is beneficial to CGP.</p>

<p>Here is a sample set of configurations needed to set up experiments using the basic GA with tournament selection and mutation (the first algorithm):
	<tt>
		breed = ec.cgp.Breeder
		pop.subpops = 1<br>
		pop.subpop.0 = ec.Subpopulation<br>
		pop.subpop.0.size = 50<br>
		pop.subpop.0.species.fitness = ec.cgp.FitnessCGP<br>
		pop.subpop.0.species.mutation-prob = .2<br>
		pop.subpop.0.species.pipe = ec.vector.breed.VectorMutationPipeline
	</tt>
</p>
<p>
	You must use the customized breeder <a href="api/ec/cgp/Breeder.html"><b>ec.cgp.Breeder</b></a> because it is responsible for computing the full expressions (phenotypes) that the integer-valued or real-valued vectors (genotypes) represent.
	The fitness class <a href="api/ec/cgp/FitnessCGP.html"><b>ec.cgp.FitnessCGP</b></a> shown above is a simple fitness that makes smaller fitnesses better.  This setting will depend on the problem you are running.
</p>

<p>If you are only using the mutation operator (i.e., you are not using crossover), then you can feed the mutation pipeline directly from tournament selection:
	<tt>
		pop.subpop.0.species.pipe.source.0 = ec.select.TournamentSelection<br>
		pop.subpop.0.species.pipe.source.0.size = 20
	</tt>
</p>

<p>Or, if you want to use crossover before mutation, then you can configure it like this:
	<tt>
		pop.subpop.0.species.pipe.source.0 = ec.vector.breed.VectorCrossoverPipeline<br>
		pop.subpop.0.species.pipe.source.0.source.0 = ec.select.TournamentSelection<br>
		pop.subpop.0.species.pipe.source.0.source.0.size = 20<br>
		pop.subpop.0.species.pipe.source.0.source.1 = ec.select.TournamentSelection<br>
		pop.subpop.0.species.pipe.source.0.source.1.size = 20
	</tt>
</p>

<p>Here is a sample set of configurations needed to set up experiments using mu+lambda ES (the second algorithm):
	<tt>
		breed = ec.cgp.MuLambdaBreederCGP<br>
		es.mu.0 = 1<br>
		es.lambda.0 = 4<br>
		pop.subpops = 1<br>
		pop.subpop.0 = ec.Subpopulation<br>
		pop.subpop.0.size = 5<br>
		pop.subpop.0.species.fitness = ec.cgp.FitnessCGP<br>
		pop.subpop.0.species.mutation-prob = .04<br>
		pop.subpop.0.species.pipe.source.0 = ec.cgp.ESSelectionCGP<br>
	</tt>
</p>
<p>
	You must use the customized breeder <a href="api/ec/cgp/MuLambdaBreederCGP.html"><b>ec.cgp.MuLambdaBreederCGP</b></a> for mu+lambda ES because it is responsible for computing the full expressions (phenotypes) that the integer-valued or real-valued vectors (genotypes) represent.
	The fitness class <a href="api/ec/cgp/FitnessCGP.html"><b>ec.cgp.FitnessCGP</b></a> shown above is a simple fitness that makes smaller fitnesses better.  This setting will depend on the problem you are running.
</p>
<p>
	We use the customized ES selection operator <a href="api/ec/cgp/ESSelectionCGP.html"><b>ec.cgp.ESSelectionCGP</b></a> for CGP because, unlike the ESSelection class that comes with ECJ, ESSelectionCGP selects among equally high-fitness individuals with equal probability.  This is important because CGP benefits greatly from <i>neutrality</i> of the evolutionary search.  Mutations sometimes occur that change the genotype but do not affect the phenotype (and thus do not affect the fitness).  These mutations are considered to have a neutral effect on the individual.  The ESSelectionCGP operator takes advantage of these effects.
</p>
	
<h3>Choose between integer-valued and real-valued representation</h3>

<p>To use the integer-valued representation, set up species and individual as in this example:
	<tt>
		pop.subpop.0.species = ec.cgp.representation.IntegerVectorSpecies<br>
		pop.subpop.0.species.ind = ec.cgp.representation.IntegerVectorIndividual
	</tt>
</p>

<p>To use the real-valued representation, set up species and individual as in this example:
	<tt>
		pop.subpop.0.species = ec.cgp.representation.FloatVectorSpecies<br>
		pop.subpop.0.species.ind = ec.cgp.representation.FloatVectorIndividual
	</tt>
</p>

<h3>Configure the CGP genome parameters</h3>

<p>Decide the number of nodes in your CGP.  This does not include the number of inputs and outputs; it only represents the number of function nodes:
	<tt>
		pop.subpop.0.species.nodes = 10
	</tt>
</p>

<p>Decide the number of input nodes in your CGP.  Your problem class will need to create the input vector and pass it along to the CGP evaluator.  If you wish to use fixed constants that are randomly generated before the evolutionary run, then be sure to include enough inputs here to accommodate the number of constants you want to include (see the section "<b>Configure the problem</b>" below for more info).
	<tt>
		pop.subpop.0.species.inputs = 5
	</tt>
</p>

<p>Decide the number of output nodes in your CGP.  Your problem class will need to analyze the outputs returned by the CGP evaluator and compute the fitness:
	<tt>
		pop.subpop.0.species.outputs = 1  
	</tt>
</p>

<h3>Configure the function set</h3>

<p>Indicate the number of functions used in your program:
	<tt>
		pop.subpop.0.species.num-functions = 4   
	</tt>
</p>

<p>Indicate the maximum arity in your function set (the maximum number of arguments used by any of your functions):
	<tt>
		pop.subpop.0.species.maxArity = 2  
	</tt>
</p>

<p>Specify the class that implements your function set.  The CGP function evaluator expects a function class that implements the <a href="api/ec/cgp/functions/Functions.html"><b>ec.cgp.functions.Functions</b></a> interface:
	<tt>
		pop.subpop.0.species.functions = ec.cgp.functions.FunctionsBreastCancerW
	</tt>
</p>
<p>
	The responsibilities of this class are to determine arity of each of your functions, compute results for each of your functions, generate descriptive display names for your functions, and generate descriptive display names for each of the inputs. 
	Read the <a href="api/ec/cgp/functions/Functions.html">ec.cgp.functions.Functions API documentation</a> for more details on the methods you need to implement in your implementing class.  See the <a href="api/ec/cgp/functions/package-frame.html">ec.cgp.functions package</a> for examples of implementing classes used for the sample problems included in this distribution.
</p>

<h3>Configure the problem</h3>

<p>The problem class is responsible for evaluating individuals to compute their fitness.  Your problem class must extend the base class <a href="api/ec/cgp/problems/ProblemCGP.html"><b>ec.cgp.problems.ProblemCGP</b></a>.  This base class provides the constant-generating methods if you need to include constants in your input vectors.  These constants are automatically generated before an evolutionary run, and remain fixed throughout the run. This is similar to the idea of <i>ephemeral random constants (ERC)</i> used in Koza-style genetic programming.<sup><a href="#r9">[9]</a></sup>  The constants are placed in the static array named <b>constants</b> in the <b>ec.cgp.problems.ProblemCGP</b> class.  Specify your implementing class like this:
	<tt>
		eval.problem = ec.cgp.problems.ProblemBreastCancerW
	</tt>
</p>

<p>If you decide to use automatically-generated constants, you can configure the number of constants and their allowable ranges like this:
	 <tt>
	 	problem.constants = 2<br>
		problem.constant-min = -1.0<br>
		problem.constant-max = 1.0
	 </tt>
</p>

<p>
	The <b>evaluate</b> method in your problem class must do the following steps to execute Cartesian genetic programs:
	<ul>
		<li>Set up the input vector.  This is a generic <b>Object[]</b> array.  Your function set will receive the generic Object[] array and is responsible for performing any necessary type checking and conversions.</li>
		<li>If you are using constants, copy them from the <b>constants</b> array to the appropriate spots in the input vector.</li>
		<li>Pass the input vector to the <a href="file://localhost/Users/doranchak/projects/work/java/stocks/doc/api/ec/cgp/Evaluator.html#evaluate(ec.EvolutionState, int, java.lang.Object[], ec.cgp.representation.VectorIndividualCGP)"><b>ec.cgp.Evaluator.evaluate</b></a> method to execute the CGP represented by the current individual's genome.  The CGP is evaluated against the input vector, and a generic Object[] array is returned.</li>
		<li>Interpret the returned output of the CGP.</li>
		<li>Repeat the above steps for every input vector required to evaluate of the given individual.</li>
		<li>Compute a fitness value, and set the fitness of the individual.</li>
		<li>Set the individual's <b>evaluated</b> flag to <b>true</b>.</li>
	</ul>
		
</p>

<a name="problems"></a><h1>Sample Problems</h1>

<a name="reg"></a><h2>Regression problems</h2>

<p>
	Three simple symbolic regression problems are included in this distribution.  You can run them using a 1+4 ES, or a standard GA with a population of size 50.  Their parameter files are:
	<ul>
		<li>Integer representation: regression-1.int.1+4.params</li>
		<li>Integer representation: regression-2.int.1+4.params</li>
		<li>Integer representation: regression-3.int.1+4.params</li>
		<li>Float representation: regression-1.float.pop50.params</li>
		<li>Float representation: regression-2.float.pop50.params</li>
		<li>Float representation: regression-3.float.pop50.params</li>
	</ul>
</p>
	<p>
		Example of how to run one of these problems:
		<tt>
			java -classpath bin ec.Evolve -file regression-1.int.1+4.params >&amp; run.log
		</tt>
	</p>
<p>
	The float representation parameter files are configured to use the new real-valued crossover operator implemented by <b><a href="api/ec/cgp/representation/FloatVectorIndividual.html#defaultCrossover(ec.EvolutionState, int, ec.vector.VectorIndividual)">ec.cgp.representation.FloatVectorIndividual.defaultCrossover</a></b>.  This operator is very good at reducing the number of generations required to find solutions to these regression problems.  For more information on this crossover operator, see <a href="#r2">Clegg et al [2007]</a>.
</p>	

<h3>Sample runs:</h3>
<table>
	<tr>
		<th colspan="4" class="title">
			Regression, 1+4 ES, Integer-based representation
		</th>
	</tr>
	<tr>
		<th>Function</th>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
	</tr>
	<tr>
		<td nowrap="yes">x<sup>6</sup> - 2x<sup>4</sup> + x<sup>2</sup></td>
		<td>9862</td>
		<td class="g"><div class="genome">0 0 1 1 1 0 1 3 0 2 3 2 2 5 5 3 0 2 0 7 7 1 6 8 1 5 6 2 5 10 11</div></td>
		<td>o0 = * (* (- 1.0 x) (+ x 1.0)) (- (* (- 1.0 x) (+ x 1.0)) (* (* (- 1.0 x) (+ x 1.0)) (* (- 1.0 x) (+ x 1.0))))</td>
	</tr>
	<tr>
		<td nowrap="yes">x<sup>5</sup> - 2x<sup>3</sup> + x</td>
		<td>1646</td>
		<td class="g"><div class="genome">0 1 0 1 1 0 2 0 0 3 2 0 0 4 1 2 2 3 2 7 7 2 0 8 2 5 6 0 7 0 9</div></td>
		<td>o0 = * x (* (* (+ 1.0 x) (- 1.0 x)) (* (+ 1.0 x) (- 1.0 x)))</td>
	</tr>
	<tr>
		<td nowrap="yes">x<sup>2</sup> + 2x + 1</td>
		<td>4</td>
		<td class="g"><div class="genome">2 0 1 0 1 0 2 2 3 1 4 3 0 4 2 1 4 1 2 1 4 2 3 3 0 6 5 0 10 6 9</div></td>
		<td>o0 = * (+ 1.0 x) (+ 1.0 x)</td>
	</tr>
	<tr>
		<th colspan="4" class="title">
			Regression, GA with pop size 50, Float-based representation
		</th>
	</tr>
	<tr>
		<th>Function</th>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
	</tr>
	<tr>
		<td nowrap="yes">x<sup>6</sup> - 2x<sup>4</sup> + x<sup>2</sup></td>
		<td>60</td>
		<td class="g"><div class="genome">0.52417827 0.37790507 0.4520471 0.7234164 0.26892883 0.55253863 0.26500112 0.4441954 0.57993037 0.7088821 0.9960679 0.9315998 0.33203906 0.6460581 0.36685604 0.5126358 0.402451 0.7975456 0.4322766 0.33529377 0.49375936 0.7679863 0.9269717 0.5833892 0.48957473 0.3691532 0.55728173 0.38734365 0.7501955 0.63142955 0.6601211</div></td>
		<td>o0 = * (* x x) (* (- 1.0 (* x x)) (- 1.0 (* x x)))</td>
	</tr>
	<tr>
		<td nowrap="yes">x<sup>5</sup> - 2x<sup>3</sup> + x</td>
		<td>117</td>
		<td class="g"><div class="genome">0.5683168 0.43130827 0.2958553 0.32524046 0.59557605 0.6701422 0.7954314 0.34086233 0.8773438 0.65244365 0.6338895 0.19503558 0.4903875 0.3036384 0.27378878 0.63068426 0.5163137 0.74592066 0.4923255 0.68086237 0.5348333 0.40979648 0.5620705 0.41622174 0.8015339 0.9388336 0.26394176 0.483493 0.54139817 0.8485149 0.59901744</div></td>
		<td>o0 = * (- 1.0 (* x x)) (* (- 1.0 (* x x)) x)</td>
	</tr>
	<tr>
		<td nowrap="yes">x<sup>2</sup> + 2x + 1</td>
		<td>1</td>
		<td class="g"><div class="genome">0.49810684 0.74828184 0.4580113 0.056411903 0.4168781 0.2537155 0.9612062 0.2232219 0.15470046 0.32732475 0.42864007 0.48914048 0.42054248 0.6635719 0.5802188 0.56591487 0.46418935 0.52750677 0.07677889 0.5247124 0.7794236 0.45044285 0.75734144 0.07277441 0.20986515 0.6957576 0.6271697 0.9085569 0.41741893 0.6546589 0.64651656</div></td>
		<td>o0 = * (+ 1.0 x) (+ 1.0 x)</td>
	</tr>
</table>

<a name="class"></a><h2>Classification problems</h2>

<p>
	There are three classification problems included in this distribution: 
</p>

	<ul>
		<li>Iris data set: This is a classic machine learning problem.  The task is to classify the species of Iris flower given a set of four numerical measurements.</li>
		<li>Breast Cancer (Wisconsin) data set: This data set originates from Dr. Wolberg at the University of Wisconsin.  The task is to classify tumors as "benign" or "malignant" based on nine numeric measurements.</li>
		<li>Breast Cancer (Yugoslavia) data set: This data set originates from Drs. Zwitter and Soklic at University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia.  The task is to classify tumors as "no recurrence events" or "recurrence events" based on nine numeric and nominal measurements.</li>
	</ul>

<p>Each sample problem uses the real-valued representation.  The 1+4 ES and standard GA algorithms are both used.  Their parameter files are:
	<ul>
		<li>breast-w.float.1+4.params</li>
		<li>breast-w.float.pop50.params</li>
		<li>breast-y.float.1+4.params</li>
		<li>breast-y.float.pop50.params</li>
		<li>iris.float.1+4.params</li>
		<li>iris.float.pop50.params</li>
	</ul>
</p>
	<p>
		Example of how to run one of these classification problems:
		<tt>
			java -classpath bin ec.Evolve -file iris.float.1+4.params >&amp; run.log
		</tt>
	</p>
<p>
	The included classification problems are configured to partition each data set into a training (learning) set and a test (verification) set.  The size of the test set is determined by the parameter <b>problem.test</b>, which indicates the proportion of the data set to reserve as the test (verification) set.  If you do not specify <b>problem.test</b>, it will default to the value <b>0.3</b>, which creates a test set out of 30% of the entire data set.  Also, the default behavior of the included classification problems is to select test set items randomly from the entire data set.  Each run will produce a different test set.</p>
	
<p>
	In the included sample runs, the Iris and Breast Cancer (Wisconsin) data sets yield the best evolved classifiers.  The evolved Iris classifier here has a 91% accuracy rate on its test set.  A previous run not included here has a 95.6% test set accuracy.  I have also seen a 100% accuracy rate on one of the test sets in a previous run, but the corresponding training accuracy was 92% and increased during the run, causing overfitting.   The evolved Wisconsin breast cancer classifier included here has a 95% accuracy rate on its test set.  A previous run not included here has a 96.2% test set accuracy.  The evolved Yugoslavia breast cancer classifier included here has only a 78% test set accuracy, which is consistent with prior research which found classifier accuracies between 66% and 78%.<sup><a href="#r5">[5][6][7][8]</a></sup>
</p>	

<h3>Sample runs:</h3>

<table>
	<tr>
		<th colspan="5" class="title">
			Iris species classification, 1+4 ES, Float-based representation
		</th>
	</tr>
	<tr>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
		<th>Test set inaccuracy</th>
		<th>Training set inaccuracy</th>
	</tr>
	<tr>
		<td>256707</td>
		<td class="g"><div class="genome">9.2709064E-4 0.75406647 0.004710436 0.3947944 0.047334015 0.2247613 0.2476747 0.5276161 0.34647346 0.89842576 0.90869254 0.10714221 0.42421746 0.8661713 0.9094378 0.21609336 0.9365554 0.17670238 0.64411956 0.9668403 0.27452952 0.343378 0.6518347 0.5355108 0.13013917 0.9232228 0.97323686 0.13311392 0.96707636 0.8409563 0.22211659 0.623666 0.0012887716 0.25371844 0.17543554 0.60922045 0.9817991 0.9248674 0.43520612 0.9892834 0.25174612 0.16985673 0.5128613 0.284437 0.04408288 0.05769229 0.8097303 0.8424388 0.33471447 0.2580859 0.024793327 0.97256154 0.041933417 0.3090365 0.20593965 0.80281967 0.7372331 0.9812226 0.659839 0.85073143 0.7221268 0.11423886 0.1851548 0.3120603 0.5396855 0.5346126 0.8922095 0.9964617 0.38210708 0.7522069 0.88827425 0.42167437 0.6923621 0.108171344 0.21751553 0.8661738 0.6466133 0.7065012 0.36531067 0.26257885 0.39809775 0.032978714 0.6991387 0.19656372 0.19690752 0.6919226 0.8938464 0.36484534 0.9858335 0.42787427 0.3205858 0.6776026 0.51768196 0.263884 0.07055658 0.33021575 0.037879467 0.97729903 0.23077917 0.2644263 0.10647082 0.22588968 0.025968611 0.3708743 0.2893222 0.17613 0.04887092 0.79006606 0.3360532 0.43181938 0.6179699 0.55602217 0.17172432 0.14294624 0.50047415 0.9552012 0.9270632 0.52080715 0.46429116 0.08913058 0.54427785 0.41234475 0.7459636 0.023207426 0.8971901 0.6675555 0.9383878 0.5859838 0.66346663 0.17341441 0.59372085 0.29338175 0.10383606 0.8917634 0.6035827 0.2760656 0.80620533 0.15257221 0.9701659 0.12403786 0.15085328 0.789956 0.18586278 0.12803978 0.9954496 0.19990939 0.95476866 0.970464 0.56703633 0.6781671 0.3718285 0.321653 0.055733085 0.34243786 0.8948598 0.16507852 0.4541763 0.75838983 0.085440695 0.6777354 0.078523815 0.054561615 0.74913067 0.7051625 0.40926683 0.8152833 0.35157079 0.300888 0.9737628 0.10224563 0.019853294 0.23091102 0.8553722 0.6438824 0.57814586 0.6285491 0.90487623 0.47093564 0.5142165 0.24117029 0.2967139 0.23669308 0.12507975 0.10444665 0.63489693 0.1831724 0.2938344 0.6162529 0.12353635 0.7474887 0.9771328 0.27746212 0.7876925 0.10682601 0.6138799 0.73141515 0.3401068 0.20765436 0.011577845 0.32174462 0.84211665 0.91117823 0.8152338 0.39887673 0.45321172 0.8646856 0.5576267 0.600747 0.6300252 0.7551802 0.17536342 0.2412973 0.93389195 0.88727736 0.5943077 0.61072373 0.84523684 0.51037925 0.120692015 0.23132938 0.5243722 0.7336111 0.42343855 0.9050872 0.5656717 0.089104176 0.038603902 0.56376773 0.20316279 0.49245518 0.37220252 0.11812711 0.9814827 0.2958747 0.4351908 0.64719784 0.9029431 0.45870835 0.35272932 0.34619612 0.70390344 0.3145674 0.30123806 0.3029278 0.99249977 0.051231503 0.70151013 0.24489164 0.21135855 0.2579432 0.8663138 0.37589514 0.17942101 0.79015946 0.29550987 0.6866144 0.8664981 0.9521103 0.6902005 0.024736106 0.278522 0.23541152 0.037877977 0.8379577 0.8738376 0.28264773 0.6705048 0.66101825 0.94618595 0.52504545 0.9216781 0.30751574 0.22739804 0.5762534 0.6499368 0.9576306 0.83480734 0.04682094 0.5774203 0.9472638 0.22667217 0.90840906 0.85008234 0.25333703 0.19007307 0.7561329 0.24861664 0.6450083 0.5211483 0.0049008727 0.88426334 0.23049253 0.035036385 0.026432276 0.86478686 0.07787323 0.50097656 0.21549755 0.034878135 0.8197827 0.29262036 0.13685048 0.7324347 0.8782935 0.8735092 0.22078043 0.19026476 0.1351695 0.271169 0.61530226 0.05940008 0.9579931 0.80944747 0.6792809 0.027973652 0.4912055 0.08224535 0.44532704 0.10777438 0.95019966 0.023929536 0.09112686 0.20489436 0.56206954 0.26676124 0.70076126 0.15414268 0.47887695 0.448129 0.4282732 0.16672069 0.33925617 0.55612504 0.56593925 0.43774885 0.4762256 0.2285347 0.69475734 0.99035764 0.90508956 0.7558544 0.69521445 0.8738755 0.7690884 0.63633263 0.4365825 0.9880699 0.86160463 0.8581177 0.8356295 0.9037484 0.5339079 0.761716 0.7895009 0.45550448 0.8688666 0.2501791 0.945031 0.68299186 0.5792785 0.29593974 0.73264474 0.72367 0.16383392 0.6250704 0.77158624 0.05025685 0.4509027 0.007910848 0.7505825 0.18776423 0.79565626 0.71921873 0.34327108 0.15265298 0.5899101 0.51072687 0.5236658 0.5500039 0.43121034 0.5098575 0.25118327 0.5485283 0.37607324 0.34926665 0.97180665 0.31897134 0.21824038 0.4226858 0.5946102 0.84030163 0.73021144 0.75692236 0.1713866 0.11184448 0.49750054 0.0039898157 0.6860583 0.9033876 0.8754127 0.057178915 0.13763022 0.20828956 0.13344091 0.051977813 0.56210953 0.72538704 0.85719424 0.49990028 0.27989012 0.53940046 0.8592741 0.7239453 0.6311716 0.90014416 0.9823597 0.2949915 0.6334043 0.631914 0.7017307 0.9527009 0.16900289 0.3379932 0.90888035 0.12507695 0.6762834 0.5600286 0.9562331 0.90136236 0.31416285 9.6839666E-4 0.76989645 0.27950478 0.5161012 0.9170804 0.30350435 0.15826362 0.87307894 0.78818965 0.3709517 0.01416415 0.75231075 0.9221637 0.8969579 0.97819006 0.7239424 0.442968 0.3108229 0.7764649 0.6334208 0.10140252 0.1164937 0.2750255 0.732971 0.81379396 0.31508976 0.82035875 0.68099535 0.969015 0.7574842 0.8226898 0.24128509 0.05655527 0.38884568 0.8959384 0.3380804 0.14583296 0.83782893 9.907484E-4 0.78211266 0.99902105 0.64771885 0.7028282 0.46115774 0.9230759 0.18030006 0.56737226 0.11539489 0.8793803 0.54610354 0.008814931 0.6938294 0.51046544 0.78491884 0.37578446 0.8824894 0.41721398 0.56026495 0.99590504 0.69111687 0.7940712 0.8124048 0.41700417 0.10307115 0.21850878 0.49930948 0.30052716 0.8360689 0.8603238 0.70956343 0.028086066 0.70247656 0.12704247 0.38567233 0.7009637 0.11335802 0.94173974 0.8335571 0.23095357 0.33499527 0.71753377 0.36224145 0.8208928 0.9178952 0.05274558 0.87241066 0.79637873 0.6519808 0.78765804 0.3558473 0.33106774 0.9629902 0.78706217 0.36015612 0.78571486 0.80909085 0.42149854 0.20680195 0.43335402 0.5370328 0.04919356 0.31169724 0.8271593 0.8512858 0.106467724 0.7857304 0.97685754 0.69900006 0.88535887 0.27617627 0.92066413 0.36155093 0.83117604 0.5047466 0.30681944 0.78453237 0.6240128 0.86052114 0.20414215 0.62458277 0.9193754 0.849136 0.5972914 0.037537575 0.554456 0.11560935 0.17218173 0.26814735 0.6872986 0.33410066 0.52376103 0.48696023 0.34312463 0.8549025 0.84428847 0.8251041 0.6811296 0.021233737 0.7494 0.95544475 0.908291 0.46300393 0.45904833 0.48105627 0.20585054 0.58772445 0.47064668 0.183806 0.49839705 0.06301743 0.7619131 0.30281007 0.5220842 0.7884641 0.9026085 0.036945105 0.35717875 0.8186501 0.99829614 0.22909999 0.99226385 0.4305657 0.39899433 0.36336094 0.8771382 0.38725638 0.31727916 0.36218292 0.3792011 0.5261922 0.7308565 0.042812467 0.7197997 0.6763933 0.7941634 0.9288341 0.87575215 0.078752816 0.23615772 0.3853017 0.540913 0.5946867 0.8850903 0.05156237 0.13759339 0.6388478 0.35459256 0.45794302 0.74361306 0.7412065 0.65732515 0.452309 0.31786758 0.14841151 0.19673097 0.7711883 0.4806422 0.44671446 0.33359635 0.067040086 0.8561975 0.4802826 0.11578429 0.8567741 0.01606065 0.40231323 0.060139 0.7068421 0.30070776 0.9027794 0.13658947 0.714204 0.60839194 0.57948285 0.21470356 0.5841124 0.29927194 0.004538715 0.25792378 0.93250716 0.6972446 0.5557619 0.58097506 0.90782166 0.48610735 0.71247286 0.36346817 0.40031612 0.09126389 0.6807408 0.9403095 0.83335567 0.9428344 0.7145516 0.89933926 0.9856436 0.4216774 0.4516238 0.61392266 0.38234603 0.91392535 0.7239676 0.9324309 0.2968533 0.4419471 0.68057024 0.21761197 0.05904919 0.20477736 0.6955854 0.30592573 0.0365144 0.9682874 0.4209093 0.73272943 0.37862802 0.47678953 0.38329804 0.16460806 0.6744999 0.93289894 0.30645758 0.23114532 0.25789732 0.7389165 0.5380543 0.59832865 0.28403997 0.5721635 0.33342302 0.48037153 0.5491271 0.31500006 0.37487727 0.14973211 0.49880642 0.25164568 0.14819092 0.7878052 0.3843668 0.5841878 0.79496837 0.35843682 0.59813046 0.33099753 0.3676932 0.6590974 0.08571547 0.37890065 0.073874116 0.09576267 0.9605572 0.5051892 0.77601653 0.44449407 0.2381463 0.27765262 0.030167937 0.108499825 0.14807707 0.09385842 0.106907606 0.5803748 0.3961516 0.8885957 0.5518452 0.59730893 0.78838795 0.09244496 0.2868408 0.46052486 0.7743393 0.75195944 0.35834742 0.800069 0.65775615 0.27063406 0.26272935 0.49950886 0.6627373 0.85666823 0.4493655 0.4698447 0.65010184 0.5947764 0.76791316 0.029884875 0.4957788 0.74009115 0.20303327 0.85326433 0.06790298 0.20942426 0.5470897 0.26822507 0.27489 0.060524106 0.4313898 0.041056633 0.5714373 0.5529217 0.4040122 0.5730813 0.06397432 0.29538155 0.66518056 0.9502267 0.54665303 0.16471046 0.027956724 0.8591343 0.39230615 0.9943317 0.7824897 0.40529978 0.012272358 0.106634736 0.19290066 0.680052 0.44102794 0.15502077 0.11935836 0.69124496 0.9147111 0.88366634 0.25205857 0.8142126 0.3864829 0.54127365 0.49140412 0.6290349 0.043471932 0.20623296 0.01641643 0.42934638 0.50502 0.7594409 0.86334735 0.6014374 0.07609677 0.08153391 0.0392372 0.21233171 0.7981926 0.24696517 0.33425587 0.61037415 0.30024868 0.330114 0.50484264 0.09235734 0.29641217 0.07602531 0.645226 0.3899294 0.78955835 0.5824631 0.53656834 0.5999215 0.55035216 0.8632852 0.23599029 0.9126038 0.20058739 0.7126961 0.099601686 0.41176158 0.37979305 0.6297936 0.19682682 0.7013928 0.8731276 0.095335305 0.99653167 0.40575427 0.51710826 0.542642 0.055417717 0.15413684 0.9229258 0.27137667 0.7804221 0.14860523 0.06680298 0.4162237 0.006085336 0.07670617 0.33372337 0.77943695 0.9263472 0.7419421 0.4727742 0.89196503 0.27076232 0.49109578 0.06757206 0.053431034 0.38776374 0.16013342 0.84323245 0.23694491 0.2186572 0.6269997 0.37158638 0.47400016 0.39984435 9.421706E-4 0.7395451 0.4231102 0.8204392 0.57163095 0.78591526 0.66073716 0.15273064 0.4280852 0.11301893 0.883397 0.6298453 0.16578066 0.11788213 0.7668163 0.875067 0.28239244 0.091827214 0.65722126 0.05904591 0.04900086 0.6834562 0.8135866 0.34469426 0.7665537 0.9160522 0.92130244 0.38536572 0.5389353 0.73694575 0.9975559 0.8847981 0.70670664 0.7713465 0.3286323 0.7549605 0.23396838 0.54581094 0.32028943 0.51836836 0.52261794 0.3674091 0.52948576 0.50560606 0.94693166 0.9230738 0.98069906 0.5924803 0.18491942 0.08701748 0.8140831 0.57316 0.7049724 0.8110285 0.6237837 0.95309156 0.45771903 0.98081964 0.3450204 0.24750173 0.06975198 0.71056026 0.70456535 0.1145547 0.8847591 0.051075816 0.21497 0.147246 0.61122334 0.5110928 0.84787816 0.15390193 0.900814 0.6912119 0.19806856 0.6954807 0.5952299 0.6419011 0.7828614 0.6793355 0.44143265 0.4275458 0.1173619 0.6448694 0.7464797 0.22791994 0.0043857098 0.64125794 0.84082186 0.12777358 0.7810962 0.6818044 0.57196736 0.83755165 0.24145484 0.5036593 0.7592254 0.015214801 0.24221689 0.61371666 0.6053086 0.93970525 0.4727403 0.12226915 0.67853194 0.718969 0.9500323 0.87571275 0.27209175 0.6863803 0.81313175 0.31537884 0.30949724 0.7530622 0.08974749 0.5528902 0.7219914 0.49692523 0.6757857 0.89622813 0.1598543 0.99609536 0.05401224 0.7440205 0.11028308 0.9253279 0.21159083 0.34969032 0.39168167 0.2603383 0.81587315 0.31842935 0.34692615 0.5435965 0.30095583 0.78543055 0.37388265 0.7357648 0.54656214 0.03304416 0.82782465 0.7146961 0.89010674 0.35223728 0.9890835 0.1552521 0.12571228 0.99017966 0.029483676 0.24758852 0.22722936 0.36289084 0.8496462 0.85789895 0.95105493 0.7698604 0.61576855 0.42356706 0.6825648 0.13802457 0.8784222 0.8623867 0.5944026 0.017744184 0.49472463 0.8379132 0.88351476 0.46449405 0.98294944 0.07476199 0.33778477 0.24644691 0.013029933 0.42833418 0.53701913 0.1822145 0.23491591 0.0116221905 0.7308401 0.87280107 0.6219202 0.06928915 0.68873614 0.5281293 0.016404152 0.1703651 0.9558104 0.28079873 0.5599264 0.2748211 0.5911181 0.7811911 0.46098447 0.33655715 0.94648063 0.008855879 0.1894337 0.8612395 0.9843483 0.5082493 0.6432326 0.30758774 0.26879144 0.9859044 0.17472422 0.15243083 0.2963423 0.78855467 0.8964752 0.42645204 0.6313522 0.6206168 0.2215727 0.25395292 0.101134956 0.87413573 0.17293447 0.074174345 0.5633502 0.53812116 0.8909114 0.9554235 0.44319922 0.7130745 0.19135845 0.65991557 0.8062222 0.7066162 0.4093964 0.34026897 0.5170132 0.34887826 0.887574 0.76572114 0.5531736 0.8570398 0.3100829 0.6991454 0.42209637 0.0751856 0.51941997 0.097665966 0.6069787 0.42494863 0.62646556 0.53317213 0.60818213 0.40667713 0.5192352 0.24272561 0.780284 0.971568 0.9030572 0.90167195 0.94164836 0.59885234 0.8606229 0.37760812 0.9367106 0.86426604 0.026730478 0.16939372 0.6145027 0.0664652 0.41228807 0.52755326 0.31466126 0.81493986 0.18283683 0.10893142 0.30350447 0.77805 0.14920479 0.9067815 0.5236348 0.606894 0.82557863 0.08074576 0.69052625 0.62565356 0.26311737 0.49986345 0.59860325 0.33619368 0.35367376 0.8733333 0.45827675 0.87101984 0.45420337 0.3627578 0.86210245 0.044695854 0.608417 0.3896045 0.16013283 0.059248984 0.20643479 0.16283321 0.046875715 0.8730272 0.86171395 0.08089602 0.46757132 0.43355787 0.039668202 0.96314186 0.017042458 0.45737225 0.36732602 0.96343935 0.5683105 0.17019838 0.3111766 0.1311202 0.4180575 0.47071016 0.70890063 0.17374974 0.83313566 0.45917284 0.9712181 0.1954028 0.07436162 0.8497254 0.77079415 0.5527391 0.77832144</div></td>
		<td>virginica = iflez (> (nor (not (&lt; petalwidth 0.8118229)) (or (iflez (if (iflez (iflez (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) -0.99178624 0.8118229) 0.1320405 (+ -0.7548797 petalwidth)) (iflez (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) -0.99178624 0.8118229) petalwidth) (not (&lt; petalwidth 0.8118229)) (&lt;= 0.3545866 sepallength)) -0.7548797)) (* (nor (+ (= 0.1320405 petallength) 0.008319378) (if petallength -1.2377005 (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)))) (and (> (if (and (&lt;= (+ 0.1320405 sepallength) (+ 0.1320405 sepallength)) (&lt;= 0.3545866 sepallength)) (* -0.7548797 (+ -0.7548797 petalwidth)) (+ -1.6926594 -0.7548797)) (* (if petallength -1.2377005 (>= (+ 0.1320405 sepallength) (+ petallength petalwidth))) (&lt; -0.7548797 0.8118229))) (if (iflez (iflez (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) -0.99178624 0.8118229) 0.1320405 (+ -0.7548797 petalwidth)) (iflez (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) -0.99178624 0.8118229) petalwidth)))) (nand (- (or (iflez 0.1320405 (- (* -0.7548797 (+ -0.7548797 petalwidth)) (nor petalwidth -0.99178624)) (nand -1.6926594 (not -1.6926594))) 0.8118229) (if petallength -1.2377005 (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)))) (> (* (+ (- (* -0.7548797 (+ -0.7548797 petalwidth)) 0.1320405) (+ (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) (+ sepalwidth (* (if petallength -1.2377005 (>= (+ 0.1320405 sepallength) (+ petallength petalwidth))) (&lt; -0.7548797 0.8118229))))) (+ (neg (&lt; petalwidth 0.8118229)) (iflez (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) -0.99178624 0.8118229))) (nand (+ petallength (iflez petalwidth (>= (&lt; 0.3545866 sepalwidth) -0.99178624) (&lt; -0.7548797 0.8118229))) (+ (- (* -0.7548797 (+ -0.7548797 petalwidth)) 0.1320405) (+ (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) (+ sepalwidth (* (if petallength -1.2377005 (>= (+ 0.1320405 sepallength) (+ petallength petalwidth))) (&lt; -0.7548797 0.8118229)))))))) (nand (nor (not (&lt; petalwidth 0.8118229)) (or (iflez (if (iflez (iflez (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) -0.99178624 0.8118229) 0.1320405 (+ -0.7548797 petalwidth)) (iflez (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) -0.99178624 0.8118229) petalwidth) (not (&lt; petalwidth 0.8118229)) (&lt;= 0.3545866 sepallength)) -0.7548797)) (nand 0.3545866 (+ (= 0.1320405 petallength) 0.008319378)))<br><br>versicolor = * (+ (- (* -0.7548797 (+ -0.7548797 petalwidth)) 0.1320405) (+ (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) (+ sepalwidth (* (if petallength -1.2377005 (>= (+ 0.1320405 sepallength) (+ petallength petalwidth))) (&lt; -0.7548797 0.8118229))))) (+ (neg (&lt; petalwidth 0.8118229)) (iflez (>= (+ 0.1320405 sepallength) (+ petallength petalwidth)) -0.99178624 0.8118229))<br><br>setosa = / (not (* (not -1.6926594) (&lt;= (+ petallength petalwidth) -1.6926594))) (neg (not (&lt; petalwidth 0.8118229))).</td>
		<td align="right" nowrap="yes">virginica: 0.06666667<br>versicolor: 0.06666667<br>setosa: 0.0<br><b>Total: 0.13333334</b></td>
		<td align="right" nowrap="yes">virginica: 0.00952381<br>versicolor: 0.00952381<br>setosa: 0.0<br><b>Total: 0.01904762</b></td>
	</tr>
	<tr>
		<th colspan="5" class="title">
			Iris species classification, GA with pop size 50, Float-based representation
		</th>
	</tr>
	<tr>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
		<th>Test set inaccuracy</th>
		<th>Training set inaccuracy</th>
	</tr>
	<tr>
		<td>24211</td>
		<td class="g"><div class="genome">0.8884938 0.4590162 0.29026085 0.6349013 0.83675945 0.8067513 0.95018625 0.6248577 0.21680295 0.80003506 0.92962205 0.24245477 0.16420126 0.8149246 0.9089544 0.67551285 0.109626055 0.18497795 0.20386797 0.20194137 0.19589281 0.05904889 0.056020677 0.5098565 0.020083785 0.45526248 0.9850884 0.2109226 0.79743654 0.9592307 0.9985112 0.04034221 0.74761343 0.32157946 0.10301012 0.94004184 0.031020343 0.5095072 0.54510534 0.53537405 0.083477736 0.5004327 0.0900954 0.2730162 0.1088379 0.8237971 0.5377268 0.24448669 0.4142176 0.3520522 0.34298462 0.36664027 0.65368754 0.8713634 0.8785629 0.91251516 0.7113433 0.030258 0.88959396 0.17206693 0.109936535 0.6928361 0.60447645 0.9417593 0.24561858 0.11361903 0.95827836 0.012165844 0.7640089 0.31394827 0.6655507 0.8315433 0.32441902 0.2776094 0.019364536 0.92050344 0.03497684 0.61240166 0.17808193 0.3447342 0.59010386 0.060377717 0.009930968 0.22641379 0.9198211 0.5606551 0.37358272 0.7761558 0.3229491 0.79965854 0.9374552 0.69431865 0.2567768 0.869662 0.8742426 0.83526033 0.23865843 0.5380701 0.85094935 0.47687405 0.89386344 0.74766934 0.6870755 0.34064794 0.034226835 0.71567607 0.21212488 0.8832305 0.009085417 0.34888452 0.3860731 0.5915423 0.3244698 0.6663621 0.9586609 0.75636166 0.96857154 0.2871477 0.73047245 0.1369943 0.28111774 0.11156714 0.8132818 0.99135077 0.13300955 0.8860093 0.39125234 0.34124422 0.91108763 0.57705295 0.16844052 0.48372936 0.8599161 0.38874048 0.9744298 0.99907243 0.5667015 0.8678031 0.7013478 0.92732567 0.016417027 0.87118405 0.63839597 0.45132762 0.5755919 0.13149405 0.40175366 0.11697042 0.38706923 0.9641277 0.48319858 0.6398467 0.85023373 0.9961002 0.8002284 0.90432954 0.3070734 0.89112145 0.14049596 0.37973255 0.07448858 0.025291026 0.65957266 0.82652605 0.004085362 0.7982269 0.6113684 0.49787503 0.76411486 0.5311243 0.33342582 0.5471416 0.76000124 0.69439447 0.44593877 0.3156796 0.41103262 0.92362636 0.7386268 0.6589474 0.5482939 0.73939675 0.78859764 0.58085877 0.36682475 0.40321058 0.24291456 0.24556017 0.12823528 0.6727357 0.90352845 0.87214506 0.066312194 0.45304763 0.1752066 0.88571477 0.45620263 0.65222293 0.81392276 0.5996027 0.6424307 0.39186364 0.16691893 0.9250217 0.77095574 0.76378006 0.5631353 0.03752041 0.7651715 0.4802422 0.26734692 0.12915546 0.08445388 0.62931913 0.26724643 0.5405494 0.48666912 0.34670812 0.88696474 4.1806698E-4 0.4940974 0.64298016 0.851126 0.32282257 0.3147691 0.22306603 0.13310367 0.4205702 0.88542837 0.9846317 0.71331924 0.10674447 0.111481905 0.6233944 0.045153916 0.013417065 0.1418947 0.2675115 0.36890185 0.8973007 0.08514792 0.89144886 0.27434278 0.8137007 0.5520514 0.41124398 0.31211668 0.8779739 0.33588862 0.69748664 0.22776502 0.56518847 0.22410142 0.9533814 0.97199863 0.90936905 0.6113276 0.59453225 0.6460179 0.70141655 0.93892324 0.79059064 0.44616157 0.4513458 0.22450686 0.97249854 0.5380106 0.25742018 0.12321824 0.30715394 0.684839 0.64028764 0.3833925 0.07117915 0.8561415 0.75088125 0.8296378 0.294217 0.39376312 0.5258616 0.6064455 0.4839111 0.030282736 0.65928155 0.79113305 0.88718057 0.16536635 0.63408136 0.16351706 0.4674294 0.24309629 0.52144957 0.9247102 0.15411973 0.76432747 0.17562169 0.37169957 0.46211016 0.55922115 0.9875502 0.6023451 0.5702912 0.7328018 0.6486029 0.44641864 0.6205352 0.36119062 0.0759511 0.45979708 0.27301288 0.8228952 0.0810079 0.9782781 0.9197464 0.7423922 0.076162994 0.25204277 0.6840719 0.36960655 0.029262722 0.09959996 0.82240885 0.55585325 0.632984 0.8523195 0.06516409 0.74202883 0.07302719 0.7878229 0.6220388 0.74535006 0.10743898 0.3617394 0.009848833 0.24894619 0.3931327 0.010418236 0.5192829 0.65335506 0.27166224 0.33586752 0.5910245 0.6931879 0.02783221 0.80377436 0.8890052 0.83466995 0.5727942 0.65172356 0.8097738 0.6758319 0.19887179 0.24088317 0.56286603 0.20550191 0.47372526 0.32439017 0.73038626 0.6807613 0.14108306 0.9585558 0.033657014 0.79079384 0.86446697 0.49645615 0.68656 0.23530018 0.94053155 0.5561267 0.2904879 0.40917987 0.82022494 0.043596625 0.2803477 0.49534184 0.8166059 0.11500549 0.35634053 0.19298542 0.35116374 0.5037795 0.77709055 0.36784762 0.5454945 0.5275087 0.39988655 0.5780804 0.7449406 0.33635426 0.6821469 0.7110023 0.5168331 0.6658985 0.4109282 0.61268395 0.94413465 0.86994576 0.85921025 0.33576608 0.899614 0.39477462 0.9329434 0.7927136 0.1758107 0.9075478 0.09544104 0.87928516 0.8541017 0.8150114 0.82280624 0.48427987 0.46100998 0.43292785 0.30063945 0.680422 0.9648574 0.8392436 0.7821567 0.41543597 0.45910674 0.7186325 0.6847063 0.93344414 0.56773996 0.50943965 0.7686304 0.32125288 0.2761165 0.4868973 0.318734 0.9152024 0.33093232 0.71967787 0.18215287 0.71516895 0.35238189 0.8091955 0.52436084 0.38610256 0.51661474 0.024370909 0.5711274 0.41454065 0.48200113 0.83788306 0.60940623 0.6130277 0.55132717 0.26845598 0.5137747 0.35570174 0.3666069 0.9583693 0.13499588 0.9811873 0.32259607 0.0774104 0.35773164 0.67178136 0.972677 0.19189692 0.95517683 0.72237533 0.70160294 0.7145635 0.9284449 0.7999015 0.055331945 0.57223266 0.9675472 0.7535506 0.48295087 0.6061327 0.3418964 0.35214382 0.5008788 0.42163032 0.42962158 0.6095031 0.8282382 0.83433455 0.97097665 0.7470733 0.51912993 0.56032586 0.3931238 0.8755882 0.9736985 0.39674652 0.018954933 0.64345706 0.9739829 0.4011917 0.6355088 0.2741477 0.41419393 0.6983966 0.9921445 0.8390373 0.35627025 0.5775915 0.4688719 0.5555084 0.6910404 0.20666307 0.9040761 0.37614423 0.875781 0.966054 0.5055647 0.96676433 0.6778345 0.41572207 0.06626707 4.900098E-4 0.4846899 0.7312343 0.03912151 0.18410039 0.9073344 0.3751279 0.4800375 0.26541376 0.20375228 0.4453926 0.77941275 0.42409152 0.17972249 0.41168576 0.6082079 0.7111859 0.56149966 0.8745416 0.5658722 0.035120726 0.4124877 0.8731226 0.7902304 0.9056264 0.03711611 0.6905836 0.48998004 0.810706 0.6601235 0.44282925 0.7081855 0.8074318 0.9334648 0.8988493 0.91004056 0.36371523 0.7194142 0.34037608 0.33686328 0.7744672 0.5699972 0.3569821 0.32701117 0.29451567 0.27176756 0.59073097 0.75963706 0.50935966 0.2574774 0.08122182 0.8163935 0.8929998 0.44124317 0.7686094 0.5597055 0.39265108 0.97823054 0.48514974 0.18182373 0.3293786 0.32592022 0.15132707 0.22681087 0.42014492 0.03489083 0.21884888 0.26726586 0.12418461 0.0068431497 0.68492025 0.74474335 0.12925899 0.09897697 0.87809515 0.60147816 0.65130883 0.39732862 0.6789058 0.3782127 0.5737552 0.4553585 0.25401342 0.96733356 0.68821454 0.93637294 0.1374163 0.5829882 0.1691345 0.8235128 0.70715636 0.48153174 0.82185954 0.8553385 0.7249389 0.33991122 0.48559046 0.61836374 0.3577389 0.8778493 0.30500323 0.19503152 0.95145184 0.37421763 0.75165004 0.8924774 0.3852889 0.5961042 0.8434879 0.028253973 0.1912927 0.6469401 0.0076642036 0.2094611 0.7749477 0.70620924 0.35195363 0.5851445 0.99349517 0.59053785 0.34536082 0.52608985 0.65716517 0.3953222 0.70409095 0.81871885 0.53688914 0.39396745 0.13415241 0.028532207 0.3334816 0.3456893 0.45132285 0.96244043 0.43145424 0.7902134 0.8838402 0.60758036 0.9002324 0.70499974 0.6108188 0.16998142 0.32125062 0.8759703 0.121804416 0.28400767 0.76916033 0.9814167 0.37732637 0.8785204 0.18072963 0.98857695 0.30149382 0.23692697 0.5276557 0.40136182 0.09089351 0.35915536 0.8160175 0.73086506 0.6589692 0.8209555 0.7854946 0.23419255 0.92455375 0.76022756 0.23583466 0.16219026 0.06501961 0.27381027 0.370179 0.6598699 0.18630469 0.109716475 0.91174185 0.27864146 0.53703636 0.96804935 0.30476695 0.17643762 0.7653219 0.47637957 0.09978813 0.08995736 0.8746586 0.9279955 0.9235563 0.9113857 0.7067483 0.65557814 0.7867228 0.03818029 0.4340396 0.19344568 0.11759859 0.93455756 0.6268513 0.5756293 0.6616364 0.013314068 0.28464645 0.40986246 0.70927066 0.07977587 0.33227676 0.4178266 0.074193 0.036784053 0.5140861 0.24542314 0.55829084 0.0823555 0.67973846 0.38347644 0.25731093 0.60691494 0.88821745 0.3997947 0.5982372 0.41722894 0.9580572 0.3793 0.23810488 0.4901086 0.27489275 0.27559817 0.07469416 0.27779394 0.13536704 0.73878855 0.9392075 0.21818554 0.482975 0.8056593 0.61169195 0.4101451 0.6194374 0.09475905 0.2315132 0.2427184 0.2007941 0.15898651 0.9545569 0.74959135 0.24509895 0.96342397 0.17367756 0.062708735 0.46851426 0.7291782 0.29504168 0.48978883 0.08941811 0.13730198 0.82899666 0.06885558 0.85601085 0.9703817 0.6696726 0.60337967 0.7007214 0.08834165 0.08800715 0.33599812 0.7464647 0.7370063 0.47208565 0.28041327 0.32330728 0.11341941 0.79984015 0.7356006 0.78530693 0.28780442 0.5953732 0.024395108 0.5529844 0.7103626 0.8654664 0.30458868 0.26301062 0.7487729 0.53854275 0.6106545 0.1885615 0.8651743 0.36089802 0.0024039745 0.4928648 0.067576885 0.29725868 0.55801517 0.87579364 0.70393485 0.8991037 0.26917416 0.9353656 0.22607845 0.2671306 0.55605364 0.69943434 0.14517075 0.48570943 0.10269189 0.25209314 0.8163362 0.8057162 0.7474105 0.119094074 0.37111986 0.78912127 0.6336157 0.66867167 0.41231626 0.65309966 0.6606234 0.43194795 0.95172524 0.5897259 0.9155809 0.91341573 0.93980736 0.5800244 0.32376754 0.65049386 0.7786148 0.3265859 0.6081456 0.626896 0.38147724 0.60379505 0.5357784 0.8857911 0.64081997 0.40396488 0.26990527 0.07282567 0.34809792 0.17848027 0.10156304 0.5789955 0.6392285 0.96296906 0.30101252 0.8524492 0.15963805 0.62970996 0.036677957 0.21395993 0.25802886 0.17346752 0.7493775 0.018615305 0.40353692 0.74737126 0.2753017 0.2845788 0.41679907 0.79523104 0.7452338 0.1960249 0.74153054 0.22794086 0.7908575 0.899995 0.24720442 0.56915826 0.7511128 0.18423188 0.07181746 0.9090673 0.21759212 0.21030807 0.6734755 0.1682778 0.6139013 0.25118566 0.24504972 0.32521677 0.15626854 0.13512784 0.9743256 0.2542687 0.5298147 0.2676639 0.5685877 0.31015027 0.70328766 0.03231597 0.8380063 0.28781736 0.7596409 0.81535953 0.9322666 0.013655245 0.28814918 0.34638244 0.4053625 0.09373301 0.27144974 0.2634918 0.9656329 0.12457645 0.6248721 0.7337341 0.5923662 0.97966397 0.16499752 0.2988783 0.59127843 0.93988836 0.13170934 0.47464573 0.10502267 0.08404064 0.70106804 0.31431967 0.65235424 0.4788229 0.2552451 0.6028239 0.44982862 0.5041441 0.37009287 0.2814057 0.33818084 0.17369795 0.25754887 0.4524498 0.69495076 0.5104697 0.23887706 0.21396941 0.815766 0.33605313 0.7443562 0.16392231 0.8867027 0.81781304 0.73573214 0.20525211 0.7817495 0.9288352 0.42940146 0.5165674 0.236027 0.4463848 0.38780618 0.043786943 0.91995484 0.1439718 0.7276719 0.78134024 0.1534487 0.18511122 0.30286485 0.9538324 0.576352 0.17605615 0.6176125 0.2802372 0.11135572 0.9533723 0.9551374 0.8556295 0.1842764 0.97095054 0.92313266 0.41169482 0.77536577 0.45831507 0.79976374 0.8375099 0.078318775 0.5376166 0.8923638 0.9977602 0.95443946 0.5770219 0.25325698 0.3403533 0.6996251 0.56296045 0.7341862 0.1995582 0.0045600533 0.43963873 0.9546588 0.9958982 0.53076106 0.639271 0.034867406 0.09773874 0.961675 0.5760879 0.50278616 0.37216097 0.51306254 0.56549263 0.32911122 0.38625222 0.02343297 0.981724 0.42419428 0.3965274 0.25733984 0.5085498 0.4280641 0.6064795 0.28830016 0.47702014 0.6431938 0.6872078 0.30806404 0.49708855 0.6475806 0.767444 0.5493213 0.96569175 0.64329994 0.2911685 0.37513137 0.35418415 0.24116641 0.11007446 0.81411684 0.0056566596 0.642622 0.3683234 0.654059 0.106077135 0.4751479 0.80969733 0.22241592 0.087433875 0.76925635 0.8306367 0.33430475 0.49163646 0.7910134 0.08244997 0.7921945 0.251554 0.5996951 0.62771726 0.29550874 0.75792706 0.96550614 0.7138368 0.51021 0.27998155 0.63699454 0.6910453 0.39265704 0.33486706 0.85171515 0.85674757 0.09864861 0.25244433 0.16452533 0.68386555 0.31813365 0.57412505 0.8861988 0.18021744 0.8314075 0.52717936 0.33561677 0.760113 0.47668362 0.32163608 0.9577466 0.30277652 0.038937688 0.17020965 0.12816161 0.85301036 0.767369 0.41610688 0.050379932 0.9470006 0.23725468 0.048484266 0.08234918 0.4979167 0.49902338 0.28334606 0.36486 0.7477142 0.33249587 0.04384172 0.3512196 0.22122115 0.8289828 0.40986472 0.10189849 0.5784586 0.35565925 0.62792724 0.55522174 0.8363256 0.23066044 0.017491639 0.6273247 0.97785234 0.55189025 0.34892744 0.6896339 0.58938 0.09888369 0.84131473 0.7037482 0.71796584 0.36893344 0.8296279 0.02178049 0.7627715 0.915958 0.71924764 0.8428965 0.10054141 0.7364174 0.5902 0.094765246 0.19005132 0.9460699 0.88147604 0.068835974 0.6271929 0.018278956 0.63828874 0.7850146 0.4432056 0.74476695 0.8664453 0.007366538 0.62314075 0.6195515 0.0038977265 0.6994981 0.37537062 0.15767926 0.29917318 0.5213633 0.47856492 0.2701288 0.72754365 0.8054018 0.7886627 0.92350763 0.77004063 0.6701425 0.30719548 0.60288906 0.045746565 0.6723222 0.8260864 0.537915 0.7643047 0.93348616 0.5004492 0.1661241 0.027339458 0.2896194 0.3789969 0.46311432 0.3463236 0.61114174 0.93346566 0.7517849 0.51639247 0.6245354 0.26253438 0.054968596 0.4825192 0.44946122 0.34789115 0.7689981 0.517568 0.67330253 0.36035872 0.106797814 0.6408074 0.3604265 0.13830239 0.797891 0.088009655 0.5216311 0.30237192</div></td>
		<td>virginica = nand (> (- (+ 1.7777395 (/ sepalwidth sepallength)) (if 0.0053305626 petalwidth -0.6896746)) (/ -0.8330147 (neg 1.6308627))) (- (* (+ (- (+ 1.7777395 (/ sepalwidth sepallength)) (- petallength petalwidth)) 1.7777395) (- petallength petalwidth)) petalwidth)<br><br>versicolor = * (nand (> (- (+ 1.7777395 (/ sepalwidth sepallength)) (if 0.0053305626 petalwidth -0.6896746)) (/ -0.8330147 (neg 1.6308627))) (- (* (+ (- (+ 1.7777395 (/ sepalwidth sepallength)) (- petallength petalwidth)) 1.7777395) (- petallength petalwidth)) petalwidth)) (- (+ 1.7777395 (/ sepalwidth sepallength)) (- petallength petalwidth))<br><br>setosa = - (+ 1.7777395 (/ sepalwidth sepallength)) (- petallength petalwidth)</td>
		<td align="right" nowrap="yes">virginica: 0.022222223<br>versicolor: 0.044444446<br>setosa: 0.022222223<br><b>Total: 0.08888889</b></td>
		<td align="right" nowrap="yes">virginica: 0.0<br>versicolor: 0.0<br>setosa: 0.0<br><b>Total: 0.0</b></td>
	</tr>
</table>

<table>
	<tr>
		<th colspan="5" class="title">
			Breast Cancer (Wisconsin) classification, 1+4 ES, Float-based representation
		</th>
	</tr>
	<tr>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
		<th>Test set inaccuracy</th>
		<th>Training set inaccuracy</th>
	</tr>
	<tr>
		<td>216602</td>
		<td class="g"><div class="genome">0.18364215 0.6973819 0.76996595 0.945526 0.5592226 0.2678743 0.9696644 0.59592855 0.20675331 0.020833492 0.29603142 0.6151906 0.016475677 0.31398302 0.527479 0.29812735 0.063449085 0.9319945 0.17730665 0.45364243 0.06751788 0.8584648 0.67777246 0.17424875 0.16689372 0.076508105 0.52168995 0.12816912 0.007546544 0.19013792 0.23409933 0.18481797 0.89522 0.20096642 0.111909986 0.33619368 0.40200257 0.6696896 0.8589009 0.9101135 0.48672885 0.8134392 0.9353189 0.85857695 0.100988984 0.15339285 0.05732262 0.2985735 0.43175918 0.3791576 0.28642762 0.74797213 0.7203979 0.97100997 0.8936057 0.31016773 0.73286474 0.6950218 0.62578666 0.9035898 0.40611917 0.9713657 0.4222982 0.19419163 0.3340277 0.666283 0.021071494 0.61026615 0.32497817 0.6730532 0.9602701 0.23456216 0.46348667 0.9679104 0.66260195 0.48067737 0.30922055 0.8372222 0.7998214 0.78381044 0.12517244 0.82448274 0.55838865 0.5902888 0.23393524 0.034107685 0.7331321 0.5296746 0.84483725 0.0026766658 0.019248784 0.13970768 0.522939 0.7020637 0.63394237 0.8775881 0.47149736 0.858864 0.848627 0.34151506 0.94830227 0.68618095 0.18108487 0.2907288 0.17002273 0.45596248 0.1802972 0.7746825 0.13259631 0.0063232183 0.35303545 0.17369014 0.69819915 0.7990901 0.93159896 0.06638032 0.336542 0.76606727 0.876056 0.6524483 0.64377624 0.8666303 0.02923268 0.35563308 0.28199983 0.5637953 0.44775987 0.55293185 0.230039 0.16016269 0.88374716 0.7876649 0.0029693842 0.51827997 0.21080542 0.015610278 0.2442298 0.9087071 0.035478532 0.874858 0.22328985 0.18752176 0.35359436 0.8270703 0.23612809 0.84030914 0.98948276 0.5800295 0.59992737 0.77737236 0.92714083 0.79902047 0.8929522 0.4592451 0.8920954 0.23935127 0.5158898 0.0023891926 0.8026509 0.6178186 0.14874882 0.117026746 0.1086458 0.23821062 0.034094512 0.6279554 0.30420125 0.6689805 0.03854066 0.012964487 0.23964876 0.14134872 0.025038898 0.56479716 0.68751454 0.70385015 0.91094464 0.28286386 0.8905976 0.932112 0.92357415 0.50676215 0.41742015 0.6464016 0.56998223 0.20529991 0.08041686 0.7599622 0.12527174 0.023652554 0.9425289 0.4762553 0.5292774 0.60819966 0.18421721 0.52889353 0.7664295 0.03430462 0.78648573 0.16941059 0.6843921 0.24580956 0.95969206 0.36321312 0.100032985 0.66436976 0.724604 0.45811588 0.9410867 0.91852736 0.03427577 0.29099643 0.3492369 0.93401754 0.097079515 0.51696706 0.9661797 0.95932 0.93622214 0.9666097 0.66928154 0.54584897 0.90860415 0.50667787 0.5000936 0.2001108 0.9439478 0.71047026 0.33201253 0.96574885 0.60073847 0.4366371 0.84040105 0.1349538 0.3479017 0.97077644 0.2739765 0.026028335 0.6065045 0.4981497 0.9299727 0.8012071 0.84704566 0.56099516 0.832939 0.5882924 0.040109396 0.0417006 0.3922161 0.52653265 0.607791 0.48674595 0.8841928 0.832336 0.7727441 0.45544112 0.051544547 0.25646126 0.3856913 0.36955488 0.37045056 0.98302305 0.8217739 0.48056936 0.05960822 0.31828183 0.46626174 0.9397423 0.3338256 0.39572686 0.5142792 0.80065733 0.47154295 0.90846866 0.74869084 0.5469975 0.97422904 0.8856365 0.47258836 0.37341392 0.16374743 0.4537043 0.345962 0.7753924 0.17125124 0.47876334 0.32525212 0.5536658 0.7945256 0.6984239 0.92030424 0.34467393 0.03464234 0.8443065 0.25869584 0.11373079 0.14730227 0.87066406 0.80648947 0.012567759 0.6407943 0.38903737 0.5680857 0.1979962 0.7475204 0.60827434 0.20171738 0.16534299 0.6451991 0.085267186 0.54957277 0.13926852 0.1468367 0.94023097 0.9559356 0.79444814 0.44815177 0.7240007 0.06319475 0.29631793 0.07530087 0.60181355 0.5423853 0.47323382 0.039624035 0.45828998 0.029012322 0.92473334 0.94140744 0.78707325 0.9300063 0.19356358 0.24758804 0.32038206 0.9493919 0.30926192 0.7174813 0.88200086 0.40607202 0.7743037 0.5895901 0.6729284 0.58174586 0.6002134 0.28901613 0.37382603 0.3391267 0.38918215 0.44183213 0.55355227 0.03903693 0.3878668 0.99074984 0.9554378 0.2627262 0.9489794 0.9540883 0.5877359 0.90022624 0.2041992 0.4662382 0.22790349 0.6582518 0.46013087 0.78310174 0.49758148 0.23775983 0.82644117 0.7036478 0.5591743 0.7514345 0.29466128 0.79811734 0.08243531 0.7298673 0.4024124 0.50652564 0.5527791 0.49625486 0.751849 0.9592842 0.13593292 0.7276612 0.91170263 0.05730909 0.40743506 0.9112645 0.13612384 0.2058407 8.639097E-4 0.4107514 0.13953936 0.67505395 0.2631011 0.96293193 0.03850794 0.32045764 0.43309224 0.14964086 0.8447451 0.64039433</div></td>
		<td>o0 = &lt;= (iflez (- (not (>= (&lt;= (- (/ clumpThickness bareNuclei) marginalAdhesion) clumpThickness) (* cellSizeUniformity -1.6005898))) (/ normalNucleoli (* (/ clumpThickness bareNuclei) normalNucleoli))) (nand cellShapeUniformity (&lt; (&lt;= (>= (&lt;= (- (/ clumpThickness bareNuclei) marginalAdhesion) clumpThickness) (* cellSizeUniformity -1.6005898)) (= (- singleEpiCellSize cellSizeUniformity) (if bareNuclei cellShapeUniformity mitoses))) cellSizeUniformity)) (- (not (>= (&lt;= (- (/ clumpThickness bareNuclei) marginalAdhesion) clumpThickness) (* cellSizeUniformity -1.6005898))) (/ normalNucleoli (* (/ clumpThickness bareNuclei) normalNucleoli)))) (* clumpThickness 1.6730318)</td>
		<td>0.052380953</td>
		<td>0.006134969</td>
	</tr>
	<tr>
		<th colspan="5" class="title">
			Breast Cancer (Wisconsin) classification, GA with pop size 50, Float-based representation
		</th>
	</tr>
	<tr>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
		<th>Test set inaccuracy</th>
		<th>Training set inaccuracy</th>
	</tr>
	<tr>
		<td>5055</td>
		<td class="g"><div class="genome">0.49543703 0.09385204 0.8071211 0.98031336 0.034612417 0.074297726 0.03561628 0.11027247 0.14654928 0.8355311 0.97768784 0.72821695 0.9184659 0.33756685 0.44457942 0.7499758 0.6501751 0.3215865 0.24035573 0.044323564 0.40316033 0.6239428 0.19182116 0.0064827204 0.35382944 0.7594341 0.45308882 0.14959896 0.6191557 0.034139097 0.4593277 0.36831832 0.9125561 0.47958976 0.7842981 0.61918545 0.8158713 0.15380275 0.9571494 0.9250915 0.38934934 0.90370274 0.65853435 0.81914544 0.52420163 0.9885719 0.67823875 0.58137393 0.44223064 0.64420563 0.7734142 0.90918493 0.5907747 0.30931962 0.13184285 0.63571686 0.5887021 0.9128804 0.047491968 0.41344637 0.1681664 0.5996575 0.26700556 0.40844363 0.91867095 0.9092392 0.9830794 0.6131958 0.936764 0.23897523 0.04094422 0.87028235 0.46200192 0.281812 0.47003764 0.2765941 0.6475944 0.48137552 0.17375273 0.30622423 0.78951526 0.7096352 0.16801894 0.55463827 0.8791919 0.109957814 0.28847963 0.98082745 0.13619864 0.0715664 0.30242234 0.9914062 0.089326024 0.70795405 0.8094319 0.70688766 0.60007095 0.4649583 0.32328594 0.106339455 0.6923162 0.26974952 0.010559976 0.115851045 0.15980262 0.9189809 0.6704318 0.39496994 0.9454949 0.4726702 0.13216573 0.16228837 0.23499638 0.44705588 0.32947707 0.7666049 0.61262417 0.7306932 0.7411099 0.766367 0.04715371 0.86875343 0.43628216 0.08354199 0.85161316 0.8317618 0.30531847 0.38038272 0.30577064 0.47552055 0.14887303 0.34885377 0.78078496 0.010268211 0.2843718 0.5065033 0.35955423 0.66089416 0.5398807 0.18568861 0.10325855 0.78225774 0.51615757 0.51198953 0.08962381 0.841451 0.36181766 0.9093722 0.52010125 0.66866916 0.42123067 0.49192464 0.6885738 0.6924121 0.080253065 0.43583167 0.55956304 0.90158874 0.9803369 0.5005222 0.88345665 0.2631061 0.9442417 0.60229206 0.9410507 0.585319 0.49623454 0.15148276 0.40986562 0.5365894 9.149313E-4 0.23787564 0.7863357 0.5734394 0.2867583 0.45285976 0.4753489 0.6075017 0.8975522 0.6095527 0.6558797 0.6457256 0.096622944 0.36785263 0.4661675 0.83715856 0.12977153 0.3889184 0.8347507 0.18053323 0.4689554 0.86605245 2.7555227E-4 0.7566049 0.80252177 0.23053545 0.011038363 0.59679615 0.087860286 0.02308631 0.15768838 0.10940862 0.17058188 0.3297885 0.0190593 0.29120302 0.65220004 0.15785694 0.48746896 0.97767824 0.97772497 0.31616813 0.8474308 0.1786431 0.26386082 0.56281286 0.5095298 0.30116683 0.75240064 0.5147477 0.6177728 0.50213623 0.15799916 0.26558602 0.23911297 0.78853923 0.22787613 0.14426649 0.91993946 0.9162498 0.26666063 0.423379 0.44773185 0.040961802 0.82769024 0.20952284 0.354985 0.51988095 0.08347976 0.6331893 0.15991831 0.72710276 0.10614967 0.018631518 0.20333254 0.98521936 0.15534377 0.1795857 0.056660473 0.184537 0.071526706 0.22037667 0.6680716 0.67970353 0.9554357 0.9716163 0.7156992 0.3924566 0.5597383 0.83249503 0.39074188 0.65096205 0.27841473 0.3436072 0.35745895 0.47038418 0.027305722 0.41984028 0.89687264 0.72675025 0.33224946 0.6910537 0.5655451 0.2671026 0.6317809 0.22557276 0.16092038 0.55112916 0.2427615 0.050150633 0.23772293 0.33245558 0.8757346 0.92034245 0.016153216 0.7559476 0.56745076 0.22258896 0.36965346 0.87988126 0.68284106 0.104705155 0.5583556 0.91704756 0.14664918 0.8114384 0.7308174 0.1259492 0.22635823 0.7078369 0.73098147 0.11610371 0.39489764 0.11187422 0.52501297 0.08586717 0.304209 0.3124448 0.9056159 0.5988934 0.82663214 0.6125967 0.99294716 0.53571486 0.82181364 0.61890876 0.71551883 0.8470111 0.6535555 0.9463137 0.71316165 0.5781163 0.04890293 0.8715368 0.7814672 0.58074546 0.06777996 0.8089013 0.49176025 0.07960725 0.9188189 0.941241 0.13456011 0.17866474 0.5589804 0.7919898 0.88141805 0.14705473 0.85705155 0.04387355 0.25322074 0.9830113 0.8111368 0.32328808 0.82623917 0.5131896 0.24742395 0.78484595 0.008438587 0.05587697 0.4055633 0.56170833 0.2556519 0.94750655 0.0063433647 0.78176296 0.16314423 0.6748869 0.285953 0.16884601 0.75330245 0.7095519 0.4183085 0.36074305 0.28501052 0.7333948 0.2270081 0.9251476 0.76776624 0.82992405 0.87646073 0.062963486 0.49958724 0.9038891 0.65208375 0.43161404 0.76842207 0.29145294 0.6250958 0.87497 0.6111521 0.91814774 0.5258381 0.01505506 0.27987587 0.6158381 0.7378966 0.7430809 0.8291562 0.739552 0.008248866 0.87994057 0.29349017 0.20959413 0.06866968 0.9477355 0.1885364 0.37896973 0.07953882 0.8101238 0.7339974 0.7024082 0.9205629 0.8958255 0.40436757 0.53774124 0.7666691 0.63613737 0.8699832 0.60978353 0.6196987 0.80827326 0.22663724 0.67667764 0.8332117 0.13030142 0.27258813 0.030747712 0.96476996 0.8468038 0.8127886 0.25614446 0.114827216 0.11719096 0.43475354 0.94340074 0.0011585355 0.54375046 0.42725462 0.48323733 0.46989757 0.17526978 0.87914556 0.6588273 0.2852481 0.21870512 0.80693454 0.5543976 0.051739752 0.5018295 0.29603547 0.95539117 0.26492113 0.22288787 0.57082963 0.9329109 0.27833778 0.83331054 0.9739453 0.69048196 0.26225936 0.8079361 0.8245191 0.15818667 0.13599235 0.9785903 0.2513333 0.12894702 0.022809267 0.5518383 0.13508546 0.6414819 0.192267 0.57565165 0.8779692 0.010336816 0.30688924 0.36674136 0.8602378 0.334247 0.90883327 0.4798178 0.94162613 0.5246616 0.62141365 0.72254217 0.19287688 0.059067667 0.6757225 0.8189358 0.5346241 0.081753194 0.4612218 0.91720706 0.3288148 0.62668985 0.032597005 0.6519133 0.9215885 0.72122246 0.28843898 0.9203851 0.2915789 0.19907212 0.78714556 0.468598 0.5672062 0.5637239 0.8640105 0.2941144 0.56870466 0.2568589 0.23033065 0.3779022 0.74803096 0.65758497 0.5662574 0.54092723 0.09889752 0.598299 0.7970133 0.28326136 0.040858924 0.17942262 0.7356282 0.4555599 0.023067474 0.5298885 0.22255325 0.5457811 0.8661175 0.2753545 0.5056912 0.34927815 0.13310176 0.5299467 0.19459069 0.665377 0.5503186 0.42854768 0.51443994 0.5766244 0.97128797 0.74124765 0.47767478 0.3653819 0.29831147 0.084122896 0.22851026 0.7489898 0.3610574 0.06526673 0.78094536 0.29806346 0.29422075 0.5470435 0.26944625 0.8646463 0.031725407 0.3478644 0.09365654 0.6342128 0.33571845 0.6404569 0.84216714 0.27548587 0.22408617 0.9775655 0.23136914 0.69698346 0.6049875 0.5122811 0.5347473 0.96972096 0.5203484 0.24138457 0.40390843 0.33808678 0.6874892 0.63452375 0.50434923 0.9407894 0.1520145 0.37693125 0.78613657 0.71467745 0.19167507 0.37708396 0.8832399 0.7803788 0.643508 0.36692327 0.5214559 0.6121705 0.05852282 0.05638355 0.683462 0.39769602 0.31581485 0.036949575 0.1490497 0.45312107 0.001085937 0.9944784 0.26308745 0.6356825 0.73024774 0.18700838 0.2975014 0.47373575 0.47259337 0.8510493 0.23467296 0.31098253 0.04114628 0.15668601 0.8048609 0.8244938 0.3224191 0.8998167 0.96907157 0.51885813 0.09020281 0.43024987 0.903912 0.2612452 0.8610208 0.6091677 0.2574008 0.5507363 0.8681575 0.017394185 0.1487475 0.9587137 0.18073654 0.9999406 0.60166395 0.15798813 0.61462015 0.556815 0.9161476 0.30633187 0.17073733 0.96476024 0.37347364 0.15506172 0.44665074 0.7491723 0.04138851 0.4223941 0.7551537 0.50200766 0.6612518 0.68097097 0.66435295 0.9542986 0.5206479 0.66549104 0.4058522 0.029045582 0.007914722 0.82475615 0.24430388 0.34001517 0.9134898 0.5755242 0.5488256 0.09529823 0.42210025 0.97262627 0.9266684 0.7828511 0.95540845 0.19237429 0.21316022 0.810218 0.3472587 0.9744793 0.26985604 0.90597993 0.2087478 0.17786467 0.9966871 0.40891343 0.6890129 0.27475673 0.25737572 0.6877243 0.04794669 0.36622894 0.578731 0.6529717 0.5312672 0.51626295 0.72649366 0.58361346 0.37188977 0.4496774 0.1277808 0.98828423 0.77600217 0.63263863 0.9320704 0.35051167 0.8106183 0.15484953 0.54416835 0.41176987 0.35142803 0.94458616 0.19717073 0.47393596 0.8922348 0.34241766 0.4226576 0.7893658 0.9953148 0.21595794 0.6889088 0.91608936 0.3181824 0.34281743 0.59384257 0.6948969 0.3009559 0.15537477 0.06743121 0.13679248 0.42674375 0.557394 0.57887906 0.61764026 0.37797308 0.368657 0.7021325 0.84041774 0.72260237 0.0010653138 0.87000304 0.27008504 0.4963349 0.51062584 0.7274208 0.15825707 0.08415717 0.7203571 0.016686141 0.46061635 0.55206543 0.7926031 0.9127831 0.23943007 0.24056089 0.5922252 0.24310058 0.019640982 0.15763533 0.671697 0.49010217 0.2109378 0.0764001 0.34837657 0.8316426 0.27480584 0.15036947 0.18395424 0.9815332 0.28349066 0.39002544 0.35695767 0.019640088 0.108939886 0.8068758 0.59096557 0.38545167 0.2594068 0.14279824 0.36203146 0.065716684 0.51904297 0.3417939 0.7954918 0.74457026 0.3901478 0.9543504 0.9514061 0.8630026 0.4460641 0.12565523 0.96339124 0.82618886 0.9604041 0.6402475 0.39679307 0.24055171 0.20122236 0.44120872 0.679888 0.69319314 0.90195 0.47862208 0.047481954 0.5642147 0.8263073 0.5043674 0.30965137 0.9959027 0.92415637 0.3139702 0.8045634 0.06803602 0.40183097 0.3425539 0.65156543 0.33253938 0.48762745 0.47802526 0.13645977 0.6109276 0.8934777 0.3649912 0.47282517 0.8432104 0.7536592 0.0702523 0.1748566 0.7435946 0.42609048 0.08309424 0.28064495 0.8588346 0.9359343 0.18326277 0.5995275 0.99781847 0.071679235 0.41394383 0.31380934 0.21969646 0.33680725 0.014200151 0.9532033 0.22858399 0.91445816 0.25858516 0.6592373 0.45949668 0.9330153 0.54081464 0.24381799 0.6094274 0.6421141 0.47785562 0.8065614 0.6378733 0.81100833 0.41265917 0.6292838 0.27688003 0.60160774 0.7179838 0.081820965 0.8013086 0.7306559 0.6943282 0.26905942 0.5910188 0.58053213 0.19108224 0.06466913 0.7250594 0.15677255 0.06681514 0.9104051 0.2577445 0.81193244 0.6113324 0.04488343 0.8439253 0.7191892 0.9819656 0.23532575 0.46628833 0.82148296 0.5434489 0.48471123 0.48277408 0.8602613 0.7040372 0.5719277 0.7575919 0.5345166 0.9940362 0.23528332 0.24104995 0.57253146 0.1473465 0.87397826 0.23788893 0.57622015 0.8085372 0.84316707 0.6848336 0.82583815 0.75440747 0.13791025 0.2892508 0.40884483 0.61273426 0.47328526 0.6598562 0.20798188 0.14106745 0.35951304 0.48300862 0.6281056 0.31296152 0.32824916 0.44995648 0.70225567 0.59866 0.88068354 0.272941 0.6652181 0.19873118 0.770093 0.45483977 0.09315336 0.5197351 0.41008013 0.18276912 0.6808864 0.07115722 0.9651073 0.6488195 0.15326864 0.5956381 0.5665158 0.21719998 0.40552402 0.97490495 0.0035237074 0.19472927 0.22810924 0.74167025 0.66176784 0.6518145 0.8555484 0.9511332 0.24960601 0.9203693 0.7267755 0.36392248 0.8040296 0.52020514 0.84570307 0.21952724 0.8583686 0.5771717 0.71500885 0.34132957 0.7964055 0.0261119 0.60832053 0.5759693 0.04643905 0.06181997 0.14163429 0.61746114 0.41114247 0.58413285 0.835863 0.90487385 0.7677164 0.35988986 0.8670325 0.13326979 0.5222444 0.77198917 0.52584773 0.50822616 0.7727674 0.14938653 0.59232223 0.30691147 0.5836592 0.039119184 0.8490991 0.078852 0.82351154 0.08562541 0.5025796 0.60920113 0.19222528 0.9311632 0.7055478 0.36645806 0.18212682 0.33797902 0.277389 0.679354 0.8763454 0.47333235 0.68895566 0.4191559 0.2520498 0.28967738 0.5864126 0.046591878 0.24414599 0.89563656 0.9293876 0.30838317 0.8502579 0.33731055 0.9583091 0.69396937 0.19578218 0.6553916 0.8074622 0.43533158 0.69590276 0.53831637 0.8045786 0.99030226 0.7312365 0.1836285 0.79438025 0.038110614 0.6994011 0.15468884 0.12525147 0.8255993 0.7591074 0.41223574 0.7297395 0.49366397 0.28986806 0.15699023 0.3984608 0.70423335 0.874792 0.18849593 0.8922311 0.85083795 0.6627537 0.34930873 0.54648954 0.23590088 0.8061616 0.4422813 0.07947081 0.44518185 0.06694728 0.56724405 0.896962 0.35063303 0.37520152 0.1289019 0.8238079 0.6781362 0.36091393 0.15499312 0.09220284 0.85812765 0.327528 0.0073615313 0.52561194 0.5833608 0.73336375 0.121813774 0.3758635 0.7526045 0.23207355 0.78504366 0.8715829 0.8378722 0.82873064 0.45309258 0.62529325 0.43845063 0.42330283 0.23134792 0.8756159 0.14586115 0.6409036 0.51688504 0.29647553 0.5613161 0.14107174 0.14975959 0.1328808 0.5951029 0.78347933 0.2615078 0.28778154 0.4455843 0.47565538 0.91375786 0.042518616 0.90380985 0.42161 0.37011814 0.83672786 0.80319864 0.5791902 0.21281415 0.37330765 0.049492538 0.9806283 0.72027606 0.56180805 0.53632724 0.96865684 0.4470979 0.45298517 0.9516675 0.95765936 0.56081665 0.9519431 0.08399123 0.68790656 0.9565774 0.33965552 0.98675096 0.14930987 0.33483684 0.9572607 0.13278323 0.77635115 0.041875422 0.89641863 0.96900624 0.2901892 0.4150641 0.9278265 0.008515298 0.7304496 0.8287855 0.6434162 0.7386012 0.08189714 0.061177254 0.70807886 0.19511801 0.39780104 0.7164511 0.6280126 0.3817792 0.48265702 0.033299625 0.19262528 0.9636095 0.8939429 0.7603013 0.45261782 0.39880747 0.8962394 0.32341343 0.7340683 0.6771861 0.715847 0.3000551 0.73036844 0.0062044263 0.43143213 0.85971063 0.87111413 0.48760992 0.93120944 0.19857526 0.93983215 0.72110736 0.22016144 0.4243688 0.20351392 0.25316417 0.7719835 0.5896674 0.39474964 0.32104844 0.2676304 0.5734138 0.8478102 0.68716115 0.015902758 0.995743 0.53953135 0.4254219 0.033367455 0.41090846 0.12000412 0.46595186 0.36561203 0.53623456 0.3077278 0.79056156 0.9623886 0.18688029 0.59021384 0.71294814 0.58026844 0.35831207 0.5088152 0.5307868 0.53393674 0.19961596 0.6310618 0.52847755 0.74421954 0.6139563</div></td>
		<td>o0 = not (nor (+ (+ (* cellShapeUniformity 1.5756812) bareNuclei) (&lt;= (> (= cellSizeUniformity 0.08695793) -1.9803793) normalNucleoli)) (nor (> (- (iflez (if blandChromatin mitoses 0.75769496) bareNuclei normalNucleoli) (* 1.97491 (+ cellSizeUniformity clumpThickness))) (> 0.08695793 singleEpiCellSize)) (nor (>= marginalAdhesion (not (- (= (> (or clumpThickness 1.5756812) (= cellSizeUniformity 0.08695793)) (+ cellSizeUniformity clumpThickness)) (* (* 1.97491 (+ cellSizeUniformity clumpThickness)) mitoses)))) (iflez (if blandChromatin mitoses 0.75769496) bareNuclei normalNucleoli))))</td>
		<td>0.04761905</td>
		<td>0.010224949</td>
	</tr>
	<tr>
		<th colspan="5" class="title">
			Breast Cancer (Yugoslavia) classification, 1+4 ES, Float-based representation
		</th>
	</tr>
	<tr>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
		<th>Test set inaccuracy</th>
		<th>Training set inaccuracy</th>
	</tr>
	<tr>
		<td>217926</td>
		<td class="g"><div class="genome">0.45706254 0.44855 0.7379725 0.15738064 0.025338054 0.016027331 0.05630386 0.87141174 0.8574841 0.024413824 0.38990897 0.8343842 0.26597798 0.50734377 0.02802062 0.17307436 0.5470952 0.20834917 0.13461101 0.81689346 0.21796477 0.3833598 0.08683431 0.4915104 0.1419949 0.6294446 0.16041249 0.5692793 0.3987348 0.2890041 0.706872 0.40626383 0.28316092 0.41008115 0.7621112 0.9081287 0.12366867 0.7606958 0.85102636 0.663859 0.6374219 0.24179202 0.7291926 0.7152364 0.052324355 0.815192 0.7361519 0.21102244 0.24193019 0.7064753 0.33945847 0.18030739 0.007996917 0.49908048 0.9859481 0.45653272 0.4798777 0.3211338 0.6818592 0.97793585 0.3592953 0.18389785 0.692623 0.56684726 0.5642125 0.99337465 0.76662934 0.61605877 0.41710937 0.9088848 0.7675927 0.8637894 0.4777829 0.88108027 0.37288022 0.46523923 0.95624095 0.67301536 0.15182555 0.6220886 0.5105178 0.45597506 0.27472413 0.6115302 0.9140842 0.6399122 0.5889659 0.651546 0.987253 0.8629507 0.11717188 0.7204221 0.50396854 0.15482217 0.65103364 0.81558543 0.14334548 0.6029058 0.7914552 0.19865185 0.103222966 0.8602644 0.7682759 0.27992386 0.6880793 0.35725605 0.47892076 0.5667568 0.29961818 0.48085916 0.5915093 0.016084492 0.9924546 0.83340985 0.59986097 0.623041 0.82690936 0.9454715 0.051816165 0.4543702 0.59277046 0.6083091 0.6861009 0.66924006 0.58068454 0.7974608 0.31799954 0.9069221 0.050308704 0.974949 0.8754808 0.72422034 0.14735794 0.6398086 0.28840595 0.14615697 0.15087855 0.9904508 0.5232056 0.728341 0.7653794 0.98716766 0.35457104 0.3666218 0.43857443 0.26622194 0.90457463 0.501277 0.45011646 0.75401866 0.67317635 0.7458376 0.51617086 0.022090375 0.5347094 0.9898064 0.24988508 0.7895108 0.53369606 0.2120505 0.58557796 0.3919763 0.06095147 0.21644771 0.90116453 0.17846572 0.85270983 0.73296815 0.76284355 0.5846398 0.8731427 0.36408234 0.6458008 0.99109733 0.7715287 0.7145366 0.98295283 0.18030375 0.20807618 0.65824133 0.40151167 0.37811875 0.23404348 0.53115106 0.29674637 0.5200664 0.45590216 0.083684444 0.98899806 0.04333639 0.54095 0.34225267 0.81521916 0.29464924 0.6632056 0.29128635 0.6342 0.3800437 0.2312473 0.37520635 0.5415346 0.9223228 0.02575314 0.46872282 0.9122749 0.6646957 0.54493225 0.03449583 0.41103888 0.24290925 0.02551192 0.98597676 0.5411377 0.7133418 0.57552993 0.7891332 0.15383285 0.034195185 0.8499494 0.84864604 0.21414644 0.298446 0.9178048 0.6166292 0.19485962 0.109105825 0.80992806 0.72822416 0.05056119 0.40080786 0.87413085 0.25397432 0.64190847 0.088188946 0.4904796 0.1217854 0.3510282 0.41034955 0.6530511 0.4057086 0.03277397 0.29039097 0.92820424 0.7549497 0.41654485 0.4653867 0.050345123 0.020687282 0.99079734 0.77954227 0.37823433 0.32366037 0.44053745 0.88265276 0.5453137 0.78848535 0.9416973 0.24980271 0.6529938 0.27148163 0.57782674 0.42334682 0.877556 0.23317695 0.61978346 0.2853576 0.2415297 0.8173181 0.74509203 0.40098506 0.9054418 0.923513 0.92982274 0.3349486 0.13389772 0.3565554 0.9839898 0.65982187 0.6152298 0.5527302 0.5755923 0.84407663 0.35842383 0.48214757 0.97377735 0.16204262 0.3202725 0.56399244 0.57789356 0.5627823 0.0068209767 0.6289287 0.49359083 0.6646535 0.5615748 0.23115295 0.9933533 0.95674306 0.0774582 0.8634063 0.041377366 0.8198993 0.17243141 0.6317455 0.42591143 0.004567325 0.08362639 0.4994527 0.118050456 0.23739362 0.25198185 0.4480055 0.7979124 0.2732727 0.8398606 0.08532679 0.67316103 0.4902708 0.32534558 0.032672048 0.28052866 0.9253309 0.7391801 0.08095068 0.9803263 0.9521597 0.10859579 0.044148803 0.22979796 0.78404516 0.8704706 0.047448516 0.9521543 0.3812328 0.09475398 0.9268719 0.89756644 0.030052185 0.13914603 0.60885364 0.55009145 0.93861115 0.12883896 0.19959617 0.97937196 0.8113834 0.54611415 0.7886024 0.8653333 0.425314 0.3068425 0.7421297 0.7810646 0.5327742 0.35787427 0.7005982 0.31841594 0.2246157 0.5507375 0.95739836 0.7935784 0.48303193 0.39558512 0.404081 0.51851976 0.71177816 0.34007627 0.42744035 0.18453681 0.75135016 0.9819681 0.6787266 0.25179082 0.3896628 0.7590399 0.29264426 0.7830089 0.16642058 0.31177086 0.96711224 0.2632941 0.8998023 0.18888557 0.11508751 0.34160995 0.1612677 0.17556828 0.06528854 0.14406806 0.36939496 0.20071876 0.81484365 0.1444971 0.06550133 0.24868846 0.39798832 0.9284008 0.2156269 0.66392416 0.73561424 0.76113325 0.6165956 0.10942614 0.5705074 0.24665058 0.73533475 0.3318752 0.90119183 0.85282713 0.9449691 0.25209123 0.54649246 0.7095669 0.5084722 0.2452507 0.24533468 0.03668356 0.4189903 0.8399883 0.74198663 0.8663529 0.2133035 0.40882212 0.8497225 0.6853708 0.9483513 0.8901405 0.3628074 0.14039296 0.051672637 0.5288558 0.12714595 0.694955 0.66900074 0.9349545 0.70606023 0.93731093 0.20749867 0.7723097 0.9028893 0.11928308 0.94933546 0.13704759 0.4714647 0.4909755 0.45485353 0.83628654 0.13624555 0.25593448 0.7888299 0.017727196 0.88683033 0.7350585 0.08816779 0.23864514 0.9449571 0.648866 0.26278716 0.45215893 0.17829227 0.059915006 0.9478311 0.48083293 0.4290303 0.74182093 0.26181698 0.29397428 0.94136715 0.66342336 0.96045065 0.6101646 0.003281474 0.07548809 0.6531679 0.27697664 0.18441367 6.776452E-4 0.65372103 0.35762984 0.78262985 0.37878144 0.60765016 0.787992 0.98398477 0.6909079 0.8444067 0.73220444 0.18067217 0.731068 0.15903711 0.5699153 0.36855328 0.4866457 0.28971118 0.60121894 0.760825 0.8729591 0.48859853 0.82593966 0.8430359 0.4744779 0.19140327 0.66367763 0.57416767 0.6215065 0.49383467 0.59143704 0.9460996 0.67640513 0.982153 0.74079907 0.24837077 0.5961321 0.0045580864 0.15703547 0.77803826 0.91001666 0.80708486 0.6524249 0.84119195 0.27563077 0.32221746 0.2371465 0.7859482 0.18297017 0.3145035 0.08972359 0.31340605 0.5731014 0.63204414 0.078336895 0.23094708 0.5986753 0.41754234 0.21517092 0.37708175 0.23598522 0.053061306 0.22109467 0.60202444 0.4581372 0.5392153 0.9415433 0.16407377 0.057284772 0.1667965 0.2909798 0.070721984 0.8885034 0.9949629 0.08955574 0.89919287 0.017260969 0.63020116 0.27280295 0.22821844 0.24970078 0.069319665 0.35233873 0.65432405 0.1659537 0.6790819 0.87100565 0.062160254 0.87778974 0.75835806 0.9473731 0.9422492 0.5477855 0.84862274 0.6240176 0.3364179 0.90681046 0.259144 0.34514308 0.21006912 0.76131266 0.4538895 0.51140696 0.13286096 0.5936278 0.21286255 0.18353868 0.45589346 0.15434486 0.27389354 0.9107307 0.21479547 0.94864905 0.26805747 0.32126862 0.10186154 0.61948 0.048882484 0.30957288 0.913641 0.3256063 0.08316207 0.7814566 0.73952276 0.79666156 0.11933535 0.10726905 0.72403485 0.41285866 0.93899405 0.9904438 0.32257748 0.04573369 0.48023945 0.73574513 0.93703896 0.29234213 0.80742306 0.54797053 0.8529284 0.46110272 0.91577625 0.6818797 0.39632 0.89147085 0.7444642 0.96363086 0.2628218 0.35712016 0.9083 0.10934067 0.58383656 0.91188467 0.6867919 0.0060159564 0.94603515 0.19587946 0.851653 0.9747171 0.6230978 0.38236552 0.8920147 0.2890492 0.26003367 0.012452841 0.110798955 0.5654439 0.15460533 0.816453 0.5208613 0.80346924 0.26993573 0.99343616 0.71210533 0.7597024 0.38014573 0.108486235 0.17737484 0.592677 0.8467294 0.3875537 0.727308 0.77993613 0.89813274 0.5356842 0.6288993 0.8458037 0.39677614 0.79621595 0.3030482 0.056055546 0.39326233 0.013120353 0.61333865 0.4803195 0.27805078 0.7167975 0.41854572 0.44373548 0.53259444 0.9764288 0.76266664 0.9302931 0.52421284 0.081864 0.0676344 0.28562576 0.2547992 0.9203078 0.93899655 0.13500887 0.54653496 0.9933198 0.9044748 0.32863092 0.68928415 0.106707275 0.8663208 0.5033091 0.8070375 0.1812101 0.16166109 0.35690844 0.6415636 0.39840424 0.115849614 0.57046324 0.5841458 0.625195 0.5514469 0.5237471 0.7568576 0.6516796 0.59795517 0.9108036 0.5976363 0.19737518 0.5270978 0.16880792 0.3445753 0.7859267 0.28287405 0.2501096 0.8103824 0.9465809 0.8737677 0.09840715 0.52802825 0.5615035 0.62791216 0.41830373 0.7963765 0.067516685 0.10996503 0.46139562 0.61533064 0.05995506 0.31686026 0.5920655 0.31258386 0.15070206 0.55736285 0.73886067 0.88924795 0.72577363 0.63812506 0.5723411 0.4084279 0.57858616 0.58689904 0.95336145 0.9433653 0.97418076 0.6976443 0.9362065 0.56985754 0.91101325 0.7587526 0.10079318 0.90552086 0.76006895 0.81213486 0.009253204 0.75610626 0.90305114 0.7574587 0.32299805 0.13390511 0.9845016 0.5637055 0.6020197 0.08905184 0.7376282 0.07246935 0.061945677 0.95885557 0.5089664 0.20508397 0.36591494 0.6997407 0.44454443 0.8803156 0.7454855 0.74296594 0.6517279 0.10834843 0.6891931 0.41564417 0.20577776 0.22962552 0.8232761 0.3976735 0.6527661 0.3034978 0.97333914 0.3298152 0.34887654 0.4439124 0.42296523 0.59952044 0.6692087 0.039865017 0.39731652 0.6072872 0.6872039 0.03524953 0.54933 0.3197829 0.5071438 0.06125444 0.6411064 0.41322178 0.04088801 0.072487 0.3548649 0.6413542 0.9010836 0.60916555 0.6125545 0.65023464 0.77937853 0.9520581 0.29034954 0.7106052 0.6790456 0.87751096 0.5722476 0.515765 0.31109077 0.97109723 0.23199475 0.12446964 0.018060923 0.52342206 0.5423077 0.44117993 0.87948 0.90668434 0.07796085 0.10648221 0.07564241 0.60687906 0.83621144 0.19395334 0.71484375 0.95992774 0.6867013 0.7777075 0.17574406 0.30755603 0.048770726 0.6346053 0.33326858 0.8338132 0.013601959 0.89175916 0.82122207 0.8032445 0.9497563 0.24399835 0.044636786 0.09556478 0.68046397 0.03735721 0.54936737 0.54324824 0.76587504 0.85485333 0.6046094 0.2764228 0.6888551 0.738701 0.82844514 0.72371143 0.02788204 0.46789682 0.9296938 0.53607327 0.8619009 0.9515718 0.35138077 0.46421558 0.29719448 0.25297153 0.46269375 0.97858673 0.48596585 0.121082485 0.48417908 0.5251068 0.31624246 0.3030749 0.029682398 0.818518 0.49441105 0.82233936 0.080073535 0.64006203 0.4486274 0.02768439 0.12138486 0.9064643 0.52993244 0.39802402 0.9045258 0.4803151 0.3711424 0.4739437 0.88764614 0.8347449 0.44841558 0.45249403 0.39265287 0.94594187 0.433604 0.495108 0.51642406 0.6158248 0.08821201 0.31750548 0.32789016 0.18445975 0.27527708 0.5919074 0.31179458 0.34494567 0.6165427 0.38830733 0.5562614 0.43658078 0.7327399 0.025043547 0.81345254 0.9130827 0.72242403 0.6039145 0.659094 0.12601233 0.84231985 0.04578489 2.7018785E-4 0.8977307 0.6200439 0.17374021 0.95123106 0.035355985 0.6073581 0.1717729 0.917609 0.93983483 0.9422368 0.098059595 0.050162494 0.63570374 0.55006975 0.8145973 0.09904051 0.7165344 0.98785913 0.33141118 0.58723515 0.7760555 0.5000158 0.7117142 0.9156983 0.0803532 0.38142598 0.9072681 0.42657882 0.52435046 0.013250828 0.23353368 0.59948945 0.8866347 0.6915623 0.6045672 0.77304417 0.984791 0.81572956 0.51156956 0.26659435 0.105252385 0.16366792 0.55900663 0.57482654 0.4030993 0.6073076 0.72698414 0.42100316 0.79985553 0.02664262 0.7556484 0.45655316 0.9197518 0.15259898 0.9137858 0.23987192 0.42653316 0.621923 0.51000625 0.019490719 0.37933815 0.7236506 0.7996296 0.46979272</div></td>
		<td>o0 = = (or (nor (iflez (&lt; irrad (&lt; breastQuad age)) nodeCaps (> degMalig (+ age age))) (nand (* (* (and (> nodeCaps (* -1.2709539 tumorSize)) (* (&lt; breastQuad age) (/ breast menopause))) (>= degMalig irrad)) (&lt; (and invNodes tumorSize) irrad)) (and invNodes tumorSize))) (or (+ (>= degMalig irrad) (&lt; (and invNodes tumorSize) irrad)) (and (> nodeCaps (* -1.2709539 tumorSize)) (* (&lt; breastQuad age) (/ breast menopause))))) (nand (>= (* (* (&lt; breastQuad age) (/ breast menopause)) (and (> nodeCaps (* -1.2709539 tumorSize)) (* (&lt; breastQuad age) (/ breast menopause)))) (= (neg age) irrad)) (iflez (if (* (&lt; breastQuad age) (/ breast menopause)) (> degMalig (+ age age)) (* (&lt; breastQuad age) (/ breast menopause))) (+ (* -1.2709539 tumorSize) (and invNodes tumorSize)) (&lt; (and invNodes tumorSize) irrad)))</td>
		<td>0.26744187</td>
		<td>0.135</td>
	</tr>
	<tr>
		<th colspan="5" class="title">
			Breast Cancer (Yugoslavia) classification, GA with pop size 50, Float-based representation
		</th>
	</tr>
	<tr>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
		<th>Test set inaccuracy</th>
		<th>Training set inaccuracy</th>
	</tr>
	<tr>
		<td>36582</td>
		<td class="g"><div class="genome">0.14299428 0.37497467 0.4632361 0.8331596 0.07216501 0.9717371 0.032799512 0.6163969 0.55867445 0.4450825 0.38564363 0.3136633 0.48299155 0.3012195 0.81763214 0.51214415 0.34110942 0.599602 0.64265174 0.53588766 0.46723193 0.42715982 0.62893397 0.22639655 0.26945746 0.6385012 0.57844424 0.5271382 0.09216267 0.7626767 0.92268276 0.38245687 0.8935799 0.680568 0.07137127 0.37693256 0.38808843 0.72959733 0.4006979 0.7233439 0.7699934 0.9715893 0.5365758 0.48704624 0.72346926 0.1523966 0.54578745 0.57848537 0.15658037 0.90395147 0.27276245 0.4546184 0.66319776 0.6721327 0.9101176 0.6951274 0.5615634 0.44009906 0.86805457 0.41931024 0.7807752 0.32613853 0.6552199 0.4324575 0.43876326 0.7294568 0.021043397 0.49047613 0.7053155 0.5502613 0.5260262 0.5563304 0.77352595 0.28772628 0.34975624 0.6814696 0.55586505 0.4137624 0.2517117 0.5171057 0.524405 0.36476734 0.4178156 0.32732743 0.6043586 0.54684067 0.3670724 0.48795614 0.6830554 0.3241503 0.34177282 0.5176674 0.5284645 0.49584618 0.48499823 0.2819264 0.5999412 0.610433 0.31885266 0.6445538 0.50321823 0.18204272 0.6135479 0.3881215 0.26416254 0.41845062 0.22093779 0.4076457 0.18284126 0.6523527 0.5442585 0.39022252 0.8859029 0.7139739 0.7305566 0.3308951 0.5182294 0.19471632 0.652367 0.5705317 0.36486673 0.5864494 0.19072425 0.5563575 0.5811282 0.17318833 0.4506951 0.3025512 0.19635463 0.90447104 0.30621272 0.46387732 0.25158048 0.30501217 0.46302584 0.7056731 0.50608265 0.5781784 0.37439415 0.6253822 0.50339675 0.65614486 0.27166748 0.5331166 0.74702024 0.7179252 0.5695206 0.67029154 0.424853 0.33877385 0.3798851 0.20551655 0.87152994 0.43579927 0.4194804 0.78178185 0.37016413 0.3036483 0.7108713 0.8220353 0.58759975 0.73377955 0.4277688 0.4674066 0.9767301 0.5359974 0.8417779 0.70827675 0.40808252 0.7226503 0.4888861 0.6340759 0.69271576 0.75760216 0.6303949 0.81047475 0.28049713 0.09028443 0.85326385 0.35058185 0.7006086 0.470662 0.4779107 0.55294627 0.46052572 0.61590576 0.6917302 0.4484079 0.3157165 0.4900533 0.75105107 0.8705967 0.5288813 0.511094 0.57402474 0.51484907 0.8482335 0.7189401 0.5427997 0.6210742 0.4638462 0.3317544 0.38322818 0.45861405 0.7138552 0.4491575 0.56393105 0.4025641 0.5992829 0.48834258 0.42249653 0.42158794 0.47388595 0.14096564 0.36375087 0.21872286 0.62759113 0.7132857 0.05304937 0.5854945 0.5745408 0.37581462 0.35186613 0.44410676 0.26164964 0.38789156 0.1100561 0.36981153 0.61763763 0.4051596 0.13095227 0.6545056 0.6055311 0.6618595 0.4060833 0.22380942 0.7890885 0.690541 0.5967492 0.20869958 0.5685123 0.3032207 0.19658418 0.55363476 0.47029033 0.44074872 0.35245118 0.40781733 0.5165775 0.34654993 0.34232366 0.48705232 0.4020807 0.5931445 0.4388102 0.4126066 0.90702415 0.51990104 0.3167697 0.6882094 0.400999 0.5465927 0.52594984 0.507696 0.4845363 0.8120551 0.6736134 0.41563806 0.48040658 0.564808 0.3296101 0.63058585 0.7230116 0.798452 0.67188585 0.19724667 0.47456145 0.6751907 0.4173332 0.5623485 0.45454046 0.22016421 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0.36704975 0.30945104 0.7907423 0.48796594 0.31106117 0.6373407 0.3249268 0.22043337 0.4413951 0.39251786 0.46237642 0.6168957 0.59541637 0.5395392 0.27560422 0.19808069 0.752725 0.5669803 0.54968077 0.5345769 0.53238904 0.47615713 0.2973093 0.16682963 0.407322 0.5837494 0.2750937 0.86263484 0.44131455 0.32624897 0.254031 0.6443858 0.25496018 0.68782866 0.3138741 0.5896524 0.6563163 0.3072877 0.6174904 0.7657682 0.7862493 0.38251898 0.6543094 0.47609687 0.76175314 0.8766582 0.56302434 0.25283033 0.69843113 0.48293227 0.57242507 0.4874883 0.526956 0.26781404 0.512337 0.51939267 0.36349344 0.5227796 0.7293977 0.21568282 0.49297696 0.3834151 0.5833036 0.40178472 0.557745 0.3927741 0.2840429 0.5187684 0.7519892 0.21190926 0.25029066 0.24891111 0.025464118 0.56920654 0.40194252 0.21034881 0.47584778 0.65941197 0.37335575 0.5895045 0.39909932 0.5433043 0.30504546 0.7161261 0.5625658 0.4424579 0.32776612 0.21715851 0.49228662 0.4503214 0.64913434 0.62050545 0.8859634 0.89556175 0.403478 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0.69184417 0.35472974 0.5442901 0.538826 0.6787976 0.4764071 0.54926515 0.32867533 0.7532578 0.44683266 0.43155026 0.50141543 0.6842868 0.18844008 0.752266 0.42676598 0.3826558 0.55194163 0.63244265 0.33264124 0.5556728 0.5877582 0.5296285 0.70496106 0.379862 0.40997514 0.55550873 0.5176113 0.65792364 0.5121758 0.6678412 0.5384357 0.2805397 0.57771045 0.4165812 0.561481 0.6173658 0.32756668 0.37412596 0.52888584 0.44588032 0.24733448 0.7149542 0.10246474 0.33472428 0.3058632 0.61915755 0.6877238 0.56534183 0.46330538 0.64266485 0.27963808 0.48690808 0.62685484 0.23382278 0.41576812 0.9325163 0.54342633 0.039112806 0.82244605 0.2202164 0.4089039 0.21190666 0.8451393 0.5420715 0.6468707 0.65328753 0.2623071 0.5035226 0.38348845 0.5043928 0.46903238 0.6836918 0.2687063 0.38226786 0.44139084 0.81435454 0.6134263 0.36557156 0.49401745 0.5383233 0.67057437 0.82754666 0.7401955 0.21532904 0.5999954 0.5758168 0.35733268 0.6534523 0.7532049 0.54302835 0.47916988 0.13413085 0.5343611 0.3185689 0.7676827 0.7260462 0.41642144 0.56210643 0.57779986 0.4558414 0.24355254 0.45726776 0.37246922 0.6908359 0.2478059 0.6562053 0.49369773 0.2855956 0.1785412 0.43629584 0.24825239 0.37921315 0.7605109 0.6244663 0.6665534 0.48993933 0.81239545 0.50026 0.32608926 0.6216322 0.4153437 0.28462866 0.40129837 0.78500646 0.6586207 0.16610566 0.4990146 0.5713083 0.34183604 0.5146906 0.25903946 0.52330124 0.48773697 0.46082872 0.34061295 0.5605222 0.20663816 0.3262207 0.653732 0.29706043 0.22710887 0.5081048 0.7622459 0.48896706 0.42800668 0.91446406 0.35030562 0.4962225 0.3847009 0.40347433 0.3736932 0.29856086 0.48713753 0.7232846 0.47690493 0.9727816 0.3436055 0.5767659 0.81217766 0.33696157 0.22462003 0.4041467 0.6966071 0.2341075 0.30096948 0.56545204 0.26132876 0.4329911 0.78421086 0.28624272 0.56277835 0.3243155 0.65959436 0.63126475 0.7636083 0.6783753 0.72207135 0.6709671 0.43799725 0.67757624 0.44053155 0.80019146 0.39945254 0.6224911 0.44090968 0.48995978 0.9169509 0.7086222 0.76716167 0.32610247 0.77920234 0.713164 0.13744813 0.4150733 0.28401992 0.32053038 0.73036057 0.46709967 0.6244014 0.36615214 0.33364543 0.57954574 0.61280715 0.7958518 0.49206355 0.4975301 0.570605 0.61424273 0.45275936 0.42861557 0.66808295 0.7050576 0.45682234 0.3987834 0.71939623 0.43042958 0.28295505 0.49352875 0.37429035 0.45463222 0.6826123 0.49733633 0.54041 0.5973668 0.53650707 0.59358805 0.2180883 0.4397422 0.25329253 0.37355694 0.41736394 0.5407522 0.5361767 0.29363266 0.18096168 0.49003172 0.3076212 0.60256827 0.86380935 0.3579214 0.543519 0.60654783 0.7060162 0.5062195 0.39115077 0.73590446 0.51497865 0.45411205 0.44948697 0.25084177 0.50505745 0.6043255 0.7429579 0.29633224 0.65438396 0.464578 0.45691854 0.5801443 0.6234316 0.64945656 0.4695617 0.4860413 0.5815216 0.4806993 0.45783055 0.6152563 0.43627283 0.68389827 0.112668306 0.50287175 0.7362486 0.60861176 0.45271692 0.69070715 0.66558826 0.43874463 0.6847943 0.6067947 0.47093666 0.53269935 0.54483396 0.4342695 0.54726267 0.2297302 0.73319405 0.42470607 0.5669065 0.6864714 0.39554438 0.7427063 0.6154653 0.59176993 0.31784415 0.56172806 0.60752714 0.7583442 0.6073719 0.5165421 0.43367398 0.77738726 0.19863811 0.8049677 0.7464901 0.4837183 0.6567868 0.6509939 0.07974299 0.7058072 0.6007073 0.71232116 0.62624437 0.6023489 0.18398184 0.5847425 0.52169526 0.3806266 0.58947605 0.7003143 0.6212196 0.4636267 0.8231157 0.40910968 0.54632 0.07984616 0.45769346 0.6268945 0.5699787 0.25765732 0.8333806 0.6129524 0.6623451 0.122162 0.31718943 0.5240531 0.29330698 0.41890657 0.37613684 0.6161616 0.50214875 0.3448086 0.5103908 0.5822519 0.41088343 0.522111 0.3376087 0.5153707 0.47703943 0.42378145 0.32995287 0.687024 0.500795 0.5634409 0.5101078 0.25215566 0.9016226 0.4368573 0.62749684 0.6524132 0.3245358 0.55318475 0.6341764 0.67579603 0.5448409 0.5417402 0.60696054 0.5872534 0.6070913 0.17292184 0.4488025 0.5614601 0.46646166 0.38085443 0.20127088 0.25992608 0.5181029 0.5896169 0.6560664 0.34798002 0.4996876 0.5849071 0.49143022 0.51818234 0.5389182 0.92869556 0.31174305 0.37962717 0.25948074 0.3363473 0.6895984 0.7528951 0.38900125 0.7461578 0.5234904 0.26211005 0.5805593 0.42309228 0.4163785 0.072317846 0.4780881 0.6193341 0.4712193 0.21679455 0.9180408 0.18391407 0.117013335 0.9063499 0.5315884 0.3229791 0.7631871 0.252569 0.6644947 0.5950658 0.5468991 0.6961491 0.72825146 0.88795793 0.27143523 0.35433233 0.5386551 0.62255615 0.33392522 0.49358493 0.36244553 0.39607108 0.19485864 0.32103258 0.5712869 0.38716426 0.6971386 0.5514826 0.28261393 0.22889423 0.72482455 0.33678836 0.3189506 0.39117274 0.559644 0.6584309 0.49752912 0.093040645 0.45110422 0.78355706 0.48626745 0.13155203 0.3990662 0.5426848 0.591971 0.50736946 0.5741275 0.16283815 0.72029144 0.36542964 0.59430265 0.37782937 0.30688387 0.46077323 0.53081053 0.3198973 0.97589266 0.5620277 0.6343967 0.50481266 0.46547663 0.27747062 0.4842449 0.61741894 0.7731898 0.22845358 0.6108008 0.7871703 0.6675161 0.36860678 0.89265823 0.29355222 0.30276638 0.24646041 0.36308455 0.66503763 0.4061509 0.80757725 0.41069654 0.33292705 0.8111833 0.7173543 0.607813 0.7757691 0.2402562 0.27877456 0.399656 0.69296616 0.39386448 0.6989975 0.48969296 0.4620356 0.49061248 0.24772468 0.5620412 0.6177038 0.5640834 0.49297988 0.45752445 0.5273963 0.32969484 0.5139731 0.4915969 0.28395998 0.5399801 0.8505358 0.16968137 0.31398445 0.8539646 0.6279719 0.55475545 0.49797976 0.60705334 0.80367357 0.40342006 0.8871977 0.5291025 0.41455406 0.5609429 0.5628438 0.6286419 0.53615195 0.49437147 0.640501 0.38784093 0.747897 0.7227568 0.22045359 0.27866632 0.54081607 0.5115064 0.056763254 0.514595 0.9016352 0.7745815 0.40323767 0.6531015 0.6165647 0.45936382 0.74112225 0.8737159 0.25234002 0.698919 0.69055414 0.5122454 0.13668694 0.7081251 0.45817766 0.32251713 0.54389524 0.65515274 0.3631567 0.4351637 0.77928984 0.5043242 0.43891022 0.8159488 0.46789187 0.3659309 0.6465191 0.306028 0.21907413 0.5549004 0.65095574 0.7057734 0.6781074 0.53112614 0.91971874 0.53654313 0.17614157 0.9995614 0.3630743 0.48897022 0.6189667 0.31835362 0.40884405 0.44388217 0.73544854 0.55834806 0.71097714 0.61371267 0.38577175 0.6393393 0.50859743 0.6001972 0.5793869 0.619671 0.37040812 0.6684379 0.49826998 0.50148666 0.33093417 0.73469514 0.5589864 0.3198743 0.6752248 0.38723898 0.6178153 0.48199758 0.64843917 0.19911553 0.6275002 0.3067376 0.6083567 0.37911296 0.66391253 0.5386535 0.7580112 0.60935885 0.97740823 0.2853005 0.468024 0.3957401 0.3955023 0.60244954 0.13410565 0.4667843 0.76960295 0.55495816 0.29469383 0.04174924 0.5020174 0.50080633 0.53724056 0.40921187 0.3560396 0.765811 0.5344028 0.5193188 0.3418382 0.25133055 0.81954235 0.283575</div></td>
		<td>o0 = - (or (= (nand irrad 0.24997258) (* nodeCaps degMalig)) invNodes) (iflez (>= (if (- (* nodeCaps degMalig) age) menopause breastQuad) age) (/ (/ (not (&lt;= 1.595995 0.24997258)) (> (= nodeCaps (* nodeCaps degMalig)) irrad)) (- (* nodeCaps degMalig) age)) (= breast (nand (> (= nodeCaps (* nodeCaps degMalig)) irrad) 0.64667153)))</td>
		<td>0.22093023</td>
		<td>0.16</td>
	</tr>
</table>


<a name="parity"></a><h2>Parity problems</h2>

<p>
	This distribution includes several n-bit even parity problems.  The task is to develop a boolean expression that correctly produces the even parity function using a fixed set of logical operators: <i>and</i>, <i>or</i>, <i>not</i>, <i>nand</i>, and <i>nor</i>.  Parity problem configurations for n ranging from 2 to 8 are included using the 1+4 ES (integer- and real-valued) and the standard real-valued GA.  The included parameter files are:
	<ul>
		<li>parity-even-2.float.1+4.params</li>
		<li>parity-even-2.float.pop50.params</li>
		<li>parity-even-2.int.1+4.params</li>
		<li>parity-even-3.float.1+4.params</li>
		<li>parity-even-3.float.pop50.params</li>
		<li>parity-even-3.int.1+4.params</li>
		<li>parity-even-4.float.1+4.params</li>
		<li>parity-even-4.float.pop50.params</li>
		<li>parity-even-4.int.1+4.params</li>
		<li>parity-even-5.float.1+4.params</li>
		<li>parity-even-5.float.pop50.params</li>
		<li>parity-even-5.int.1+4.params</li>
		<li>parity-even-6.float.1+4.params</li>
		<li>parity-even-6.float.pop50.params</li>
		<li>parity-even-6.int.1+4.params</li>
		<li>parity-even-7.float.1+4.params</li>
		<li>parity-even-7.float.pop50.params</li>
		<li>parity-even-7.int.1+4.params</li>
		<li>parity-even-8.float.1+4.params</li>
		<li>parity-even-8.float.pop50.params</li>
		<li>parity-even-8.int.1+4.params</li>
	</ul>
</p>
	<p>
		Example of how to run one of these parity problems:
		<tt>
			java -classpath bin ec.Evolve -file parity-even-4.int.1+4.params >&amp; run.log
		</tt>
	</p>

<h3>Sample runs:</h3>

<table>
	<tr>
		<th colspan="5" class="title">
			Even N-parity, 1+4 ES, Integer-based representation
		</th>
	</tr>
	<tr>
		<th>N</th>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
		<th>Output</th>
	</tr>
	<tr valign="top">
		<td>2</td>
		<td>255</td>
		<td class="g"><div class="genome">1 1 1 1 0 2 4 0 2 1 4 4 4 4 5 0 3 2 1 5 4 4 3 4 2 4 0 0 7 5 9</div></td>
		<td>o0 = nand (or x0 (or x1 x1)) (nand x0 (or x1 x1))</td>
		<td>1 0 0 1</td>
	</tr>
	<tr valign="top">
		<td>3</td>
		<td>2632</td>
		<td class="g"><div class="genome">3 2 1 1 3 0 0 2 4 4 4 1 3 0 2 1 7 5 3 8 1 0 6 6 0 4 8 0 8 1 3 12 9 0 10 8 4 11 10 3 4 4 0 16 1 0 9 17 2 7 11 4 12 9 3 3 16 0 8 21 13</div></td>
		<td>o0 = nor (and (or (nor x0 x2) (and x2 (or (nor x2 x1) x0))) x1) (nor (or (nor x0 x2) (and x2 (or (nor x2 x1) x0))) x1)</td>
		<td>1 0 0 1 0 1 1 0</td>
	</tr>
	<tr valign="top">
		<td>4</td>
		<td>4964</td>
		<td class="g"><div class="genome">0 3 3 0 0 2 3 2 0 0 4 1 0 4 5 3 6 4 4 8 2 3 6 4 1 5 0 2 6 7 4 1 6 3 1 4 1 15 15 1 8 15 3 5 14 1 10 16 3 5 7 4 9 4 1 7 5 1 0 2 2 1 10 0 2 4 0 20 18 3 12 16 3 18 20 1 15 8 2 27 24 4 22 18 4 23 20 3 24 27 3 4 32 1 22 6 0 35 31 4 36 21 4 12 28 2 9 30 1 5 32 4 17 14 3 9 38 4 17 36 4 39 42 4 20 32 4 21 4 0 25 5 0 46 31 4 19 5 0 29 28 2 12 20 2 28 48 4 23 24 3 14 24 2 47 7 4 15 38 4 8 56 3 40 54 2 16 16 4 24 22 2 29 41 4 12 22 1 59 50 4 17 40 4 38 16 2 2 4 0 9 27 4 36 63 0 28 23 3 43 29 3 9 32 0 42 45 0 60 37 2 4 27 4 68 65 0 69 59 3 76 39 4 13 18 4 38 71 3 10 1 2 7 60 2 37 2 1 8 23 4 8 7 2 62 18 1 80 43 0 45 55 4 75 86 4 0 20 2 26 73 3 89 74 2 91 61 2 16 39 0 44 19 1 92 77 1 49 71 1 8 7 1 0 85 0 78 82 2 50 59 2 78 6 1 23 97 0 12 67 88</div></td>
		<td>o0 = nand (nand (nand (and (or (or (and (and x3 x3) x1) (and x0 x2)) (nor x2 x0)) (nand (or (and (and x3 x3) x1) (and x0 x2)) (nor (and x0 x2) (nand x1 (nor x2 x0))))) (or (not (or (nor x1 (and x3 x3)) (nor x1 (and x3 x3)))) (and (or (nor x1 (and x3 x3)) (and (and x3 x3) (and x0 x2))) (nor (nor (and x0 x2) (nand x1 (nor x2 x0))) (nor (and x0 x2) (and (and x3 x3) x1)))))) (nand (nand (or (and x0 x2) x0) (nor (nor (and x0 x2) (nand x1 (nor x2 x0))) (nor (and x0 x2) (and (and x3 x3) x1)))) (or (nor x1 (and x3 x3)) (nor x1 (and x3 x3))))) (or (nor (nand (and (and x3 x3) (and x0 x2)) x2) x1) (nand (or (and (and x3 x3) (and x0 x2)) (nor x1 (and x3 x3))) (and (or (or (and (and x3 x3) x1) (and x0 x2)) (nor x2 x0)) (nand (or (and (and x3 x3) x1) (and x0 x2)) (nor (and x0 x2) (nand x1 (nor x2 x0)))))))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1</td>
	</tr>
	<tr valign="top">
		<td>5</td>
		<td>16117</td>
		<td class="g"><div class="genome">4 3 2 1 2 3 0 5 1 1 0 4 3 1 5 2 3 5 2 1 6 0 0 4 4 10 1 2 9 6 3 7 9 4 15 6 4 4 5 4 17 4 2 8 15 2 0 7 1 6 11 4 10 0 0 16 21 2 16 12 3 12 19 3 23 25 0 13 5 3 0 15 0 23 25 3 15 16 0 29 9 0 16 20 1 32 1 2 11 33 0 4 2 0 3 28 3 6 22 4 7 31 3 15 6 2 13 3 3 11 38 4 15 1 1 41 15 3 18 16 3 38 12 2 40 26 1 15 46 1 6 38 2 27 33 1 24 42 4 50 43 4 44 49 0 25 42 0 8 25 3 29 26 1 39 55 1 33 27 3 16 40 2 46 31 3 55 45 1 46 59 2 60 3 1 25 33 1 21 20 3 12 48 1 22 52 1 20 53 1 12 66 0 49 25 3 25 17 4 23 55 1 36 10 0 9 56 1 30 68 3 27 17 4 35 48 1 22 6 1 27 28 3 73 70 4 42 64 0 45 56 4 19 6 2 18 36 2 58 1 0 18 25 0 22 57 4 56 55 2 59 32 0 74 65 0 31 51 4 57 47 0 24 22 3 3 14 3 38 24 3 47 67 3 59 39 4 31 21 0 69 6 2 85 87 2 95 85 4 99 68 4 51 35 2 20 23 0 23 13 62</div></td>
		<td>o0 = not (nor (nor (and (and (nand (nor (and (nand x3 x2) x1) (nor x1 (nand x3 x2))) (or x2 x3)) (or (or x2 x3) (not x1))) (nor (and x0 x4) (not (or x0 x4)))) (nor (and (nand (nor (and (nand x3 x2) x1) (nor x1 (nand x3 x2))) (or x2 x3)) (or (or x2 x3) (not x1))) (nor (and x0 x4) (not (or x0 x4))))) (nor (nand (and (nand x3 x2) x1) (and (and (and (nand (nor (and (nand x3 x2) x1) (nor x1 (nand x3 x2))) (or x2 x3)) (or (or x2 x3) (not x1))) (nor (and x0 x4) (not (or x0 x4)))) (nor x1 (nand x3 x2)))) (and x0 x4)))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0</td>
	</tr>
	<tr valign="top">
		<td>6</td>
		<td>1091430</td>
		<td class="g"><div class="genome">1 3 5 0 2 1 0 2 4 3 0 0 1 4 2 2 2 2 4 7 6 0 3 5 3 4 6 3 6 7 1 2 1 1 15 13 3 0 6 0 8 1 0 12 16 4 18 13 0 15 17 4 11 3 3 9 9 4 17 12 0 11 14 2 5 10 0 17 20 1 26 5 3 20 17 1 28 30 3 4 0 0 17 25 2 32 4 4 34 12 3 0 34 0 5 8 3 35 25 0 32 5 3 32 33 0 40 31 0 41 0 0 28 7 4 6 23 2 22 12 4 19 13 2 39 39 0 9 19 1 45 12 4 33 46 3 14 35 4 49 3 4 4 24 3 23 41 3 51 25 3 14 48 0 16 3 4 57 39 1 56 5 3 41 53 2 47 14 3 39 36 2 57 16 1 41 60 0 4 34 0 65 40 2 21 28 4 21 11 1 34 31 4 44 17 4 12 13 0 11 26 0 21 13 3 7 9 0 39 1 4 17 63 1 12 31 0 30 59 0 28 8 4 26 32 1 60 53 0 31 8 0 25 54 2 7 70 3 78 7 2 67 23 0 65 70 1 37 9 1 86 58 3 66 64 3 52 39 0 34 54 3 30 27 4 64 81 2 24 26 3 64 89 0 94 69 0 69 46 3 81 35 1 84 1 4 89 84 3 38 84 4 69 30 2 83 91 0 59 98 1 0 68 3 67 7 1 89 74 3 101 16 4 88 58 4 89 0 1 5 40 0 48 32 3 61 14 4 38 61 0 86 35 0 88 29 1 93 99 2 54 48 4 68 98 1 81 58 4 41 0 1 112 15 0 30 55 4 122 80 3 6 66 1 3 33 0 85 124 4 53 81 1 39 65 1 82 87 3 75 45 2 6 130 3 52 14 1 103 51 0 16 44 2 22 1 2 89 116 1 129 81 4 62 138 2 97 117 2 89 26 0 138 113 0 125 58 0 94 36 2 94 77 4 3 22 3 46 5 4 120 133 4 91 90 3 1 111 1 12 149 1 11 131 4 47 89 4 27 70 4 1 7 4 95 64 2 49 70 0 11 71 2 153 18 4 32 153 0 142 71 2 8 126 3 18 153 2 127 140 2 132 12 1 4 38 4 25 7 2 123 87 0 85 18 3 75 56 4 103 58 4 42 19 3 122 113 2 102 29 0 48 168 2 121 58 4 34 58 0 112 117 2 5 119 3 128 118 2 114 92 1 141 32 0 174 85 4 106 126 0 142 116 0 22 58 2 177 113 0 8 109 4 117 82 3 34 163 2 128 112 1 133 141 3 97 156 4 98 147 0 112 126 4 88 84 3 0 193 3 79 169 4 167 95 0 39 133 3 111 66 1 148 137 4 162 20 4 174 68 141</div></td>
		<td>o0 = not (and (nand (or (and (nor (nor x4 x0) (and (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (nand (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (nand (and x2 x1) (or x3 x5))))) (or (and (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (and (nand (and x2 x1) (or x3 x5)) (or x2 x1))) (nor (and (nand (and x2 x1) (or x3 x5)) (or x2 x1)) (or (nor (or x3 x5) (and x2 x1)) (and x3 x5))))) (nor (and (nor (nor x4 x0) (and (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (nand (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (nand (and x2 x1) (or x3 x5))))) (or (and (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (and (nand (and x2 x1) (or x3 x5)) (or x2 x1))) (nor (and (nand (and x2 x1) (or x3 x5)) (or x2 x1)) (or (nor (or x3 x5) (and x2 x1)) (and x3 x5))))) (nand x4 (nor (nor x0 x0) (nor x0 x0))))) (or (nor (and (nor (nor x4 x0) (and (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (nand (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (nand (and x2 x1) (or x3 x5))))) (or (and (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (and (nand (and x2 x1) (or x3 x5)) (or x2 x1))) (nor (and (nand (and x2 x1) (or x3 x5)) (or x2 x1)) (or (nor (or x3 x5) (and x2 x1)) (and x3 x5))))) (nand x4 (nor (nor x0 x0) (nor x0 x0)))) (nand x4 (nor (nor x0 x0) (nor x0 x0))))) (or (not (nor x4 x0)) (or (and (or (nor (or x3 x5) (and x2 x1)) (and x3 x5)) (and (nand (and x2 x1) (or x3 x5)) (or x2 x1))) (nor (and (nand (and x2 x1) (or x3 x5)) (or x2 x1)) (or (nor (or x3 x5) (and x2 x1)) (and x3 x5))))))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1</td>
	</tr>
	<tr valign="top">
		<td>7</td>
		<td>354301</td>
		<td class="g"><div class="genome">1 5 2 1 4 4 0 1 0 0 3 8 3 7 6 4 2 5 4 7 6 1 13 9 2 11 5 3 10 6 3 0 1 1 12 1 1 10 17 2 18 8 3 3 8 4 0 18 3 21 0 2 22 10 2 12 17 0 9 13 0 10 17 0 12 26 1 19 14 4 14 19 0 27 15 3 26 12 4 29 30 0 15 33 3 24 20 3 28 32 3 33 21 4 21 19 1 37 11 2 31 5 1 15 37 0 37 11 1 32 26 0 34 21 4 25 2 1 33 18 0 8 29 3 42 27 0 39 38 3 24 5 2 32 16 0 48 10 2 28 4 0 48 49 0 48 44 4 39 9 0 34 31 0 13 10 0 9 58 3 48 22 4 19 56 2 49 4 4 37 13 4 24 34 3 7 14 2 62 29 3 44 54 4 21 28 2 23 42 4 19 60 2 16 47 3 57 36 1 9 66 3 46 29 4 55 53 4 67 72 3 40 12 1 58 65 3 19 18 2 69 23 1 3 14 4 24 29 1 16 8 4 13 26 3 43 0 3 4 18 2 83 10 0 29 58 0 47 45 4 73 18 0 66 37 4 13 90 0 77 65 3 26 92 2 54 64 0 35 13 3 40 92 3 41 66 2 97 80 4 99 67 4 51 59 1 3 98 4 87 62 1 40 80 2 34 55 0 81 96 2 99 5 4 15 95 0 11 103 0 14 42 1 25 90 2 98 52 2 21 6 1 12 28 1 52 57 0 72 72 4 95 13 1 56 97 2 61 101 4 80 96 3 36 7 2 4 70 4 56 12 1 98 83 2 81 56 0 43 41 3 16 29 3 85 70 4 57 52 2 114 37 1 127 30 4 87 64 1 85 16 2 95 123 0 75 49 1 113 21 2 25 62 3 108 64 3 92 29 1 45 22 0 43 84 1 9 129 0 95 128 0 121 26 3 132 107 0 77 95 2 64 140 4 138 120 1 9 70 3 92 53 2 41 129 4 100 36 4 2 116 4 76 13 3 48 66 2 3 4 1 88 15 3 39 115 0 60 34 4 87 43 3 147 95 2 66 45 0 55 142 0 22 94 2 18 151 3 115 15 0 46 42 1 25 78 4 17 63 1 33 41 3 17 117 1 146 102 2 153 139 0 76 152 4 117 131 0 75 66 1 18 80 2 162 131 4 98 54 2 58 161 4 59 66 0 20 130 1 78 25 4 6 180 3 39 41 1 68 91 3 112 122 0 2 51 2 31 13 1 69 62 1 169 42 2 45 147 0 145 92 1 104 96 3 84 183 2 192 195 0 31 58 0 196 64 1 26 129 1 46 58 3 27 79 4 150 189 1 96 163 0 172 3 0 27 54 0 120 40 174</div></td>
		<td>o0 = and (nand (nor (and (and (not (nor (or x5 x2) x6)) (nand (or (or (and x3 (or x4 x4)) (nor x0 x1)) (or (nand (or x5 x2) x6) (and x1 x0))) (nand (or (nand (or x5 x2) x6) (and x1 x0)) (or (and x3 (or x4 x4)) (nor x0 x1))))) (nor x3 (or x4 x4))) (and (nor (and (nor (nand (or (or (and x3 (or x4 x4)) (nor x0 x1)) (or (nand (or x5 x2) x6) (and x1 x0))) (nand (or (nand (or x5 x2) x6) (and x1 x0)) (or (and x3 (or x4 x4)) (nor x0 x1)))) (nor x3 (or x4 x4))) (nor (or x5 x2) x6)) (and (and x3 (or x4 x4)) (nor x0 x1))) (and (or (nor (nand (or (or (and x3 (or x4 x4)) (nor x0 x1)) (or (nand (or x5 x2) x6) (and x1 x0))) (nand (or (nand (or x5 x2) x6) (and x1 x0)) (or (and x3 (or x4 x4)) (nor x0 x1)))) (nor x3 (or x4 x4))) (nor (or x5 x2) x6)) (nand (nor x3 (or x4 x4)) (or (and x3 (or x4 x4)) (nor x0 x1)))))) (nor (and (and (not (nor (or x5 x2) x6)) (nand (or (or (and x3 (or x4 x4)) (nor x0 x1)) (or (nand (or x5 x2) x6) (and x1 x0))) (nand (or (nand (or x5 x2) x6) (and x1 x0)) (or (and x3 (or x4 x4)) (nor x0 x1))))) (and (and (and x3 (or x4 x4)) (nor x0 x1)) (not (nor (or x5 x2) x6)))) (nor (and (nand x2 x5) (and (and x1 x0) (nand (or x5 x2) x6))) (nor (and (and x1 x0) (nand (or x5 x2) x6)) (nand x2 x5))))) (nand (nand (not (nor (not (and (and (and x3 (or x4 x4)) (nor x0 x1)) (not (nor (or x5 x2) x6)))) (nand (nand (or x5 x2) x6) (nand (or (and x1 x0) (not (not (and (or (nor (nand (or (or (and x3 (or x4 x4)) (nor x0 x1)) (or (nand (or x5 x2) x6) (and x1 x0))) (nand (or (nand (or x5 x2) x6) (and x1 x0)) (or (and x3 (or x4 x4)) (nor x0 x1)))) (nor x3 (or x4 x4))) (nor (or x5 x2) x6)) (nand (nor x3 (or x4 x4)) (or (and x3 (or x4 x4)) (nor x0 x1))))))) (or (nand x2 x5) x1))))) (nor (and (and (not (nor (or x5 x2) x6)) (nand (or (or (and x3 (or x4 x4)) (nor x0 x1)) (or (nand (or x5 x2) x6) (and x1 x0))) (nand (or (nand (or x5 x2) x6) (and x1 x0)) (or (and x3 (or x4 x4)) (nor x0 x1))))) (nor x3 (or x4 x4))) (and (nor (and (nor (nand (or (or (and x3 (or x4 x4)) (nor x0 x1)) (or (nand (or x5 x2) x6) (and x1 x0))) (nand (or (nand (or x5 x2) x6) (and x1 x0)) (or (and x3 (or x4 x4)) (nor x0 x1)))) (nor x3 (or x4 x4))) (nor (or x5 x2) x6)) (and (and x3 (or x4 x4)) (nor x0 x1))) (and (or (nor (nand (or (or (and x3 (or x4 x4)) (nor x0 x1)) (or (nand (or x5 x2) x6) (and x1 x0))) (nand (or (nand (or x5 x2) x6) (and x1 x0)) (or (and x3 (or x4 x4)) (nor x0 x1)))) (nor x3 (or x4 x4))) (nor (or x5 x2) x6)) (nand (nor x3 (or x4 x4)) (or (and x3 (or x4 x4)) (nor x0 x1))))))) (nor (and (nand x2 x5) (and (and x1 x0) (nand (or x5 x2) x6))) (nor (and (and x1 x0) (nand (or x5 x2) x6)) (nand x2 x5))))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0</td>
	</tr>
	<tr valign="top">
		<td>8</td>
		<td>941936</td>
		<td class="g"><div class="genome">3 1 5 4 6 3 0 5 1 1 4 2 4 6 9 4 7 0 2 8 0 1 0 7 4 2 4 4 12 13 0 13 15 3 18 9 0 15 17 0 18 9 3 12 6 3 19 21 3 23 6 3 4 12 1 6 3 1 23 10 1 18 24 3 13 24 0 16 11 0 15 3 3 3 22 0 26 27 1 7 4 4 10 27 0 18 32 1 33 30 4 29 22 3 36 33 2 37 34 0 26 7 4 10 23 0 11 1 4 14 42 4 16 6 4 7 18 4 13 23 2 44 41 4 33 19 2 47 31 3 40 44 3 36 43 1 8 40 0 20 46 0 13 34 0 55 32 2 7 2 3 14 47 2 26 42 0 53 15 2 14 51 1 47 40 4 24 17 2 48 9 1 27 26 4 11 61 2 41 21 4 46 21 1 30 39 4 65 9 0 69 33 0 65 51 1 3 12 2 9 11 1 43 48 4 41 23 4 25 70 2 54 68 4 0 21 3 71 40 3 29 23 4 1 9 4 72 80 1 44 4 1 15 83 1 74 82 2 82 17 4 66 40 0 74 52 4 12 86 4 63 20 2 31 3 1 2 27 0 5 13 1 43 28 2 94 1 2 86 52 4 95 97 4 52 31 1 65 51 2 87 43 4 18 55 3 62 99 4 2 28 4 74 72 0 10 93 4 100 83 3 48 80 3 45 40 2 11 89 1 108 99 1 72 47 0 105 67 1 75 69 4 31 44 0 57 27 0 98 87 3 85 75 3 108 107 4 114 42 3 2 35 1 48 16 2 27 72 3 122 70 1 11 85 1 96 12 1 41 0 1 124 62 0 0 71 0 67 125 2 3 119 4 83 11 0 64 105 3 5 46 0 95 39 3 55 15 1 112 84 2 124 4 4 48 133 1 102 104 4 18 59 0 45 122 1 40 82 1 1 27 2 40 48 1 20 65 1 15 128 1 110 20 0 137 124 2 43 89 1 110 100 2 115 122 2 5 64 4 70 15 0 98 81 0 129 114 3 94 53 2 121 65 2 7 158 4 139 9 0 85 114 2 91 19 4 94 146 3 70 83 1 157 123 3 64 148 0 69 39 2 58 60 1 0 152 0 150 24 3 134 33 3 12 44 3 65 94 1 130 66 1 71 96 1 168 114 2 104 127 3 114 17 3 120 92 3 168 95 2 11 4 0 123 174 3 68 42 1 29 75 1 40 12 3 46 97 4 148 51 4 3 41 1 119 75 2 109 168 3 175 170 1 103 24 4 169 24 3 82 178 2 193 133 3 18 56 3 69 15 2 184 89 1 31 121 0 54 12 1 46 173 4 39 113 1 171 109 1 167 177 3 157 137 4 186 29 3 190 52 0 141 149 3 77 66 3 55 185 3 158 146 3 66 52 2 73 153 4 40 61 2 43 32 3 140 195 4 198 2 1 189 91 2 50 174 3 123 168 3 18 113 0 207 184 1 86 161 3 175 187 3 119 178 0 31 24 1 9 8 3 190 200 2 96 48 1 215 33 3 23 116 1 198 32 4 122 157 4 196 98 1 228 191 4 73 127 1 103 120 2 34 80 3 28 201 1 102 203 3 148 156 1 28 50 3 169 27 0 161 233 0 137 113 3 10 50 4 216 215 2 208 137 3 230 64 4 115 89 1 74 94 0 155 184 1 116 75 4 78 189 0 237 4 4 69 113 0 55 79 3 164 238 3 137 229 1 231 116 4 192 211 4 101 169 4 171 172 3 21 183 3 113 149 3 82 174 2 46 76 2 48 236 0 106 96 4 84 79 2 224 144 3 59 20 2 178 251 1 223 0 0 188 258 1 214 57 1 53 2 3 217 181 2 191 15 3 232 71 4 15 209 3 246 176 3 232 63 4 161 57 4 72 9 0 20 164 3 230 228 0 127 186 3 275 277 4 66 44 2 103 176 0 93 175 2 84 291 0 32 36 4 135 59 4 27 198 1 173 13 4 278 45 4 32 270 2 180 235 4 129 227 1 222 298 2 47 276 1 119 177 0 248 79 0 178 213 0 147 2 225</div></td>
		<td>o0 = nor (nor (nor (not (nand (not (nor x1 x5)) (nand (and x5 x1) (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3)))))) (nor (and (or (and (nand x2 x4) (or x4 x2)) (nor (and (and (nand x7 x0) (or x0 x7)) (nor x3 (nor (nand x6 (nand x6 x3)) x6))) (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1))))) (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1)))) (not (or (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1))) (and (nand x2 x4) (or x4 x2)))))) (nand (or (or (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1)) (or x6 x3)) (nor (not (or (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1))) (and (nand x2 x4) (or x4 x2)))) (nand (not (nor x1 x5)) (nand (and x5 x1) (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))))))) (nand (and (or (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1)) (or x6 x3)) (nor (not (or (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1))) (and (nand x2 x4) (or x4 x2)))) (nand (not (nor x1 x5)) (nand (and x5 x1) (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))))))) (nor (and (or (and (nand x2 x4) (or x4 x2)) (nor (and (and (nand x7 x0) (or x0 x7)) (nor x3 (nor (nand x6 (nand x6 x3)) x6))) (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1))))) (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1)))) (not (or (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1))) (and (nand x2 x4) (or x4 x2)))))))) (nor (or (or (and (or x4 x2) x1) (not (nand (not (nor x1 x5)) (nand (and x5 x1) (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))))))) (or (and (nand x2 x4) (or x4 x2)) (nor (and (and (nand x7 x0) (or x0 x7)) (nor x3 (nor (nand x6 (nand x6 x3)) x6))) (and (or x6 x3) (or (nor (nor (and (nand x7 x0) (or x0 x7)) (nand x6 x3)) (and (and (nand x7 x0) (or x0 x7)) (nand x6 x3))) (and x5 x1)))))) (nand (nand x6 (nand x6 x3)) (nand x7 x0)))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1</td>
	</tr>
	<tr>
		<th colspan="5" class="title">
			Even N-parity, GA with pop size 50, Float-based representation
		</th>
	</tr>
	<tr>
		<th>N</th>
		<th>Generations</th>
		<th>Genome</th>
		<th>Expression</th>
		<th>Output</th>
	</tr>
	<tr valign="top">
		<td>2</td>
		<td>292</td>
		<td class="g"><div class="genome">0.08667368 0.5519368 0.15836787 0.1002692 0.35883248 0.25645757 0.6832642 0.033074796 0.31612986 0.3001957 0.81647366 0.4221533 0.5004363 0.5325524 0.92426443 0.3867377 0.65569156 0.5361141 0.8094461 0.42056835 0.6032945 0.9910905 0.03160602 0.82651 0.40506363 0.85666734 0.77360505 0.974231 0.8386894 0.7476518 0.48269826</div></td>
		<td>o0 = or (nor x0 x1) (and x1 x0)</td>
		<td>1 0 0 1</td>
	</tr>
	<tr valign="top">
		<td>3</td>
		<td>3490</td>
		<td class="g"><div class="genome">0.9941635 0.15520298 0.28757071 0.1146574 0.45745248 0.733586 0.82878065 0.42145163 0.7107931 0.21883124 0.8452327 0.033933997 0.6305621 0.23153824 0.0031154752 0.7846093 0.2789377 0.00376302 0.4379691 0.09588957 0.56120884 0.14033663 0.17813617 0.5301835 0.20763063 0.9720173 0.015573144 0.29122543 0.8253896 0.0887745 0.64446783 0.16986698 0.8075758 0.2620446 0.65426636 0.5863159 0.22108966 0.01694268 0.018835068 0.018855333 0.028958559 0.94096 0.49811798 0.4491573 0.67028534 0.40266913 0.36749488 0.08519524 0.94067097 0.920344 0.6119343 0.67924 0.6936548 0.2211585 0.48738998 0.03862846 0.66254336 0.27545983 0.3926537 0.46356505 0.10426551 0.89201427 0.74979615 0.32949919 0.1174832 0.2967074 0.50851095 0.6827832 0.14950633 0.3228807 0.42190737 0.4649291 0.40620553 0.9986916 0.84062326 0.4607914 0.72365105 0.4662484 0.22412503 0.78229976 0.2421729 0.5683912 0.75277823 0.7491288 0.67289233 0.41554224 0.36251456 0.53849936 0.6184041 0.2010116 0.017192006 0.93582255 0.78959817 0.36914873 0.97157675 0.48250198 0.748313 0.3226574 0.8964775 0.95523053 0.79553324 0.07138175 0.29437155 0.8725721 0.41592193 0.6639383 0.5820823 0.47787255 0.6960209 0.06935662 0.7321029 0.48264438 0.24386281 0.010674417 0.35632026 0.7087599 0.9741741 0.14510971 0.7046806 0.51180255 0.67041504 0.02868855 0.9662188 0.76857793 0.16161758 0.26829368 0.80133474 0.17205662 0.018467426 0.81778014 0.8802964 0.1703518 0.5945307 0.91419953 0.26603633 0.10095769 0.015268743 0.74856526 0.49245232 0.31530356 0.9576566 0.9533778 0.35340327 0.95837057 0.9099829 0.06698668 0.56822485 0.03808993 0.5185223 0.14320463 0.48559952 0.893747 0.35342175 0.64761794 0.56246173 0.80188805 0.7759855 0.93769646 0.95579207 0.94463664 0.8908207 0.13011259 0.93701345 0.27493787 0.5081793 0.62503886 0.34430778 0.96179265 0.98129123 0.5379064 0.06293172 0.015301943 0.9599894 0.5073782 0.039519608 0.4250148 0.29837656 0.57265085 0.3758229 0.47276032 0.58517116 0.64947337 0.84608424 0.5830652 0.5260682 0.49717778 0.05639577 0.9542126 0.70660645 0.10896355 0.6840183 0.7443924 0.24971896 0.14287561 0.3945368 0.8012372 0.6814774 0.93481594 0.50095606 0.447877 0.8607495 0.86717045 0.24050283 0.29291463 0.38869363 0.052779913 0.9168452 0.14356035 0.69842446 0.68189806 0.8335676 0.98630774 0.7171331 0.7048537 0.5117168 0.9322741 0.5411155 0.3783803 0.21097142 0.024672687 0.5097222 0.1383993 0.7691578 0.5105554 0.12654495 0.9021401 0.32598102 0.08093488 0.5987358 0.4158901 0.7489414 0.40148097 0.1807313 0.8256484 0.19960916 0.4687308 0.055746138 0.06618428 0.8760201 0.35975963 0.90653473 0.96024054 0.31792814 0.9536356 0.921533 0.4157564 0.73825324 0.57045406 0.42953354 0.978437 0.8232909 0.60005015 0.47365665 0.7524644 0.5287564 0.5614539 0.07022411 0.29388523 0.9479649 0.5789794 0.11513358 0.3363738 0.26587135 0.998098 0.18113989 0.50336665 0.24129814 0.052862585 0.23389941 0.9575422 0.93959934 0.44619644 0.8547352 0.33782423 0.30423564 0.7720511 0.9091004 0.69999856 0.27334404 0.37675214 0.8669951 0.54059935 0.67730993 0.17459345 0.8589195 0.8160931 0.021163642 0.72046703 0.2213198 0.44025683 0.6474191 0.2936834 0.17032641 0.09394479 0.007204294 0.14137185 0.48615038 0.90154606 0.44285035 0.6922525 0.8391479</div></td>
		<td>o0 = nand (nand (and x0 (or x0 x0)) (nor (nor x2 (and x1 (nand x2 (nand x0 x0)))) (and x1 x2))) (nand (nand x0 x0) (not (nor (nor x2 (and x1 (nand x2 (nand x0 x0)))) (and x1 x2))))</td>
		<td>1 0 0 1 0 1 1 0</td>
	</tr>
	<tr valign="top">
		<td>4</td>
		<td>2022</td>
		<td class="g"><div class="genome">0.7433263 0.27790952 0.8081023 0.1676979 0.39306957 0.68873537 0.516244 0.124177694 0.2711218 0.052274168 0.46941316 0.9969408 0.7484511 0.5627267 0.68498707 0.4768321 0.8326403 0.49256027 0.46992952 0.724547 0.8425317 0.9551169 0.5541473 0.4194634 0.8279349 0.3494969 0.22322327 0.25383043 0.106292605 0.9260351 0.21130782 0.5914017 0.4372328 0.68578345 0.4787562 0.72353935 0.2806306 0.10952115 0.98516667 0.26776052 0.76833445 0.5145404 0.97827125 0.34380138 0.4736421 0.084986985 0.9489369 0.7415394 0.38057953 0.3155139 0.8840883 0.5688389 0.71317536 0.79947245 0.15900981 0.8429485 0.7791809 0.13962275 0.86808205 0.4499532 0.8633 0.2414496 0.86755085 0.89092153 0.64723295 0.42054665 0.6229475 0.16680253 0.27657586 0.6258154 0.42160714 0.83464855 0.38446158 0.21719879 0.16869617 0.82896525 0.6363821 0.19346118 0.8001373 0.65196174 0.08207208 0.026688933 0.7855068 0.37653267 0.596619 0.96067274 0.12236446 0.33106875 0.8330011 0.32527715 0.6969636 0.10182339 0.22179794 0.07730979 0.83301944 0.6234659 0.4678864 0.5857513 0.17627668 0.8906392 0.24279529 0.6015456 0.7362825 0.37670416 0.57408977 0.8382256 0.7435008 0.46809298 0.8091111 0.25737584 0.9778204 0.5543404 0.38938147 0.27568185 0.27230507 0.059619248 0.4541083 0.95984083 0.80335265 0.5485628 0.023043156 0.96229947 0.68549985 0.21150863 0.4299106 0.8324556 0.88780105 0.06257695 0.056527317 0.8045079 0.5924493 0.94125575 0.14317447 0.9214373 0.9395402 0.92319655 0.71178126 0.47557086 0.7267714 0.6499278 0.1580649 0.20256287 0.57811165 0.63132924 0.58273333 0.051549554 0.7110335 0.33778435 0.43966955 0.26573575 0.013618112 0.97548276 0.7694714 0.8997361 0.118053794 0.37650198 0.062286854 0.3575467 0.48953843 0.11059332 0.30903077 0.5391804 0.9428042 0.17723513 0.71593285 0.6333738 0.31608975 0.10318607 0.15467691 0.61094606 0.19717234 0.9357031 0.43789893 0.61668444 0.120459855 0.2632751 0.036017954 0.87182105 0.09477031 0.7154957 0.28930736 0.42076284 0.46314657 0.9039573 0.7514715 0.9363247 0.23182005 0.040664554 0.031440437 0.64584863 0.5208573 0.3719864 0.106552005 0.5926336 0.802727 0.47132826 0.41651684 0.26904857 0.9572279 0.4635489 0.23932976 0.780731 0.428685 0.032982588 0.69959104 0.5898277 0.54642826 0.6661097 0.1843059 0.9082879 0.40141648 0.7361812 0.23996216 0.5154612 0.37593883 0.1525799 0.6094425 0.4076417 0.6200566 0.36946785 0.77996147 0.285752 0.44361466 0.41646177 0.2999516 0.5920604 0.19189042 0.8176911 0.72372276 0.7150198 0.41869444 0.6360031 0.39887148 0.14334357 0.29856235 0.80515987 0.03410399 0.80608165 0.64679086 0.32690358 0.6309984 0.6397166 0.18871248 0.19977438 0.5099821 0.71761394 0.90366435 0.93042916 0.4889061 0.6024141 0.830257 0.20041174 0.79113245 0.21230787 0.15570879 0.10382706 0.04194057 0.09181708 0.40229183 0.9503688 0.4363271 0.33410072 0.58945537 0.8658485 0.22763836 0.70483416 0.43166965 0.9795326 0.6824646 0.77631754 0.18832463 0.90071636 0.99506384 0.18765676 0.6277767 0.88026595 0.33135247 0.961281 0.11682111 0.6974831 0.41559535 0.26228404 0.27421588 0.40390378 0.50202185 0.64224935 0.5357848 0.2260639 0.955722 0.29873115 0.17376786 0.36410898 0.784402 0.70727134 0.37142926 0.27186054 0.7109543 0.29074985 0.16781771 0.3793596 0.46750426</div></td>
		<td>o0 = and (and (or x2 (and (nand (not x0) (nor (nor x1 x3) (and x1 x3))) (or (nor (nor x1 x3) (and x1 x3)) (not x0)))) (nand (and (nand (not x0) (nor (nor x1 x3) (and x1 x3))) (or (nor (nor x1 x3) (and x1 x3)) (not x0))) x2)) (or (and (nand (not x0) (nor (nor x1 x3) (and x1 x3))) (or (nor (nor x1 x3) (and x1 x3)) (not x0))) (nand (nor (nor x1 x3) (and x1 x3)) (and (nand (not x0) (nor (nor x1 x3) (and x1 x3))) (or (or x1 (nand (nor x1 x3) x2)) (nor (nor x1 x3) (and x1 x3))))))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1</td>
	</tr>
	<tr valign="top">
		<td>5</td>
		<td>20569</td>
		<td class="g"><div class="genome">0.15910584 0.39692062 0.69483626 0.5356263 0.043334186 0.09871191 0.6496949 0.6094113 0.38576978 0.043125093 0.49921405 0.2822402 0.56047857 0.82711726 0.023987353 0.9616755 0.21947938 0.47299707 0.8773309 0.95832556 0.31015444 0.5595516 0.3136142 0.34581363 0.6729486 0.39362872 0.69743437 0.6118136 0.07389104 0.2812395 0.48851788 0.89195335 0.0201118 0.16882354 0.5888713 0.36688846 0.20462775 0.026083767 0.5952164 0.83731854 0.47531533 0.43147022 0.83775604 0.042003095 0.7243054 0.17911112 0.55479974 0.7162914 0.29263157 0.9523733 0.730233 0.4029793 0.41203147 0.93962646 0.32615358 0.6479897 0.7076259 0.14306986 0.4384061 0.007330239 0.93966603 0.549703 0.97058403 0.9177198 0.6600727 0.6061887 0.3512239 0.9688197 0.9133165 0.036494732 0.9299945 0.5889154 0.19919324 0.68344283 0.31905496 0.11090571 0.66480845 0.7847358 0.7693993 0.098911405 0.20611984 0.5335948 0.7374176 0.23752165 0.5469467 0.04325056 0.18765748 0.007640779 0.84975684 0.7704314 0.49592406 0.69292 0.28742188 0.7308079 0.3911035 0.8829325 0.19835407 0.10284299 0.86431444 0.3553903 0.1702547 0.98218495 0.5949163 0.8663499 0.895838 0.69797444 0.88175756 0.963252 0.39771867 0.0077153444 0.1941325 0.8954422 0.71518 0.651147 0.9523292 0.9597108 0.8030993 0.244605 0.46505672 0.122267365 0.33344036 0.51073945 0.21078336 0.94131255 0.59835094 0.43357325 0.0052839518 0.3074258 0.015294313 0.41968513 0.8341002 0.90577024 0.8624796 0.6703216 0.95721745 0.9180252 0.98115337 0.85925764 0.6112367 0.8042031 0.6410316 0.097266614 0.69592524 0.73061043 0.36684608 0.94405067 0.21430928 0.66295177 0.68151605 0.68869823 0.6077445 0.77754015 0.33470762 0.4474421 0.50387007 0.6252068 0.36217344 0.8671423 0.85004896 0.66613156 0.42402017 0.3939796 0.16030765 0.87314045 0.7383085 0.5807486 0.9707121 0.028781295 0.18011564 0.37843716 0.039478183 0.52892524 0.3925962 0.2465586 0.12374467 0.99977404 0.41315657 0.20727038 0.46283782 0.34820992 0.6677601 0.2769165 0.7441889 0.84092253 0.16372466 0.8769938 0.9926172 0.052573502 0.6615511 0.39190394 0.10993534 0.90462244 0.56455874 0.094332874 0.8212759 0.51132464 0.0886873 0.39988333 0.47561347 0.12273508 0.7381446 0.37272608 0.2524824 0.8573491 0.8406327 0.35512722 0.8499458 0.4828058 0.05337161 0.85897446 0.7433184 0.8168861 0.22446913 0.8618776 0.73121285 0.741486 0.37545753 0.80200595 0.5696934 0.5274319 0.8554133 0.50110084 0.15341532 0.2115019 0.7348178 0.011128843 0.44001085 0.38869113 0.10399681 0.48997986 0.035431862 0.8979604 0.38723594 0.52298176 0.91070396 0.9425368 0.48119658 0.88840955 0.9454145 0.2252531 0.5202535 0.2657736 0.9989794 0.45574325 0.5775043 0.8695607 0.8146825 0.7568494 0.69877386 0.44946253 0.24738425 0.14075243 0.3634916 0.8536505 0.91507584 0.11023432 0.5317182 0.15527284 0.6955607 0.81386256 0.14520591 0.26345575 0.34120226 0.59980375 0.9795479 0.2242431 0.49862647 0.8706919 0.23528981 0.92474896 0.61774904 0.16254699 0.1576634 0.85029167 0.9840306 0.75631595 0.80560637 0.24440962 0.14174795 0.77758527 0.9300429 0.581064 0.12419158 0.12371606 0.22081673 0.4199015 0.756008 0.7998509 0.477852 0.66221255 0.3818363 0.33645368 0.37125492 0.17025822 0.9281532 0.94742495 0.63686657 0.8156764 0.6216893 0.44461852 0.4770723</div></td>
		<td>o0 = nand (nand (not (or (nor x1 x3) (and (not (nor x4 x2)) (and x1 x3)))) (nand (or (nand (or x0 (nand x2 x4)) (not (nor (and x1 x3) (not (nor x4 x2))))) (and (nand x2 x4) x0)) (nand x0 (nor (and x1 x3) (not (nor x4 x2)))))) (nand (and (nand x0 (nor (and x1 x3) (not (nor x4 x2)))) (or (nor x1 x3) (and (not (nor x4 x2)) (and x1 x3)))) (or (nand (or x0 (nand x2 x4)) (not (nor (and x1 x3) (not (nor x4 x2))))) (and (nand x2 x4) x0)))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0</td>
	</tr>
	
	<tr valign="top">
		<td>6</td>
		<td>64078</td>
		<td class="g"><div class="genome">0.15495974 0.0848102 0.4092269 0.8182737 0.7963491 0.5156302 0.23446935 0.020768642 0.33170348 0.6926027 0.17446476 0.97115207 0.8232182 0.34123427 0.16026056 0.60917705 0.47858745 0.472737 0.042707384 0.087026 0.73525196 0.93755025 0.27008295 0.89394516 0.89577734 0.41884482 0.54171216 0.7767609 0.66507775 0.8600056 0.7863111 0.9851741 0.3078243 0.40828645 0.4869299 0.8361878 0.3403309 0.45390332 0.6992839 0.8222111 0.74907523 0.7332516 0.40483195 0.2930693 0.43651634 0.72230965 0.29419833 0.6652391 0.08145398 0.35082632 0.8683101 0.3669526 0.45256734 0.7708873 0.07453889 0.17889833 0.640265 0.22373664 0.2613153 0.97939265 0.15943164 0.97559637 0.031412303 0.81865805 0.95313364 0.8194533 0.8460353 0.23787963 0.86164486 0.6324212 0.8289874 0.92256504 0.97379386 0.44609225 0.94723547 0.032848597 0.16673231 0.7021036 0.9172342 0.17519456 0.0480088 0.2497598 0.82821757 0.92077136 0.8068564 0.053251386 0.58424854 0.3554275 0.7906382 0.11921954 0.23152691 0.069669604 0.38182008 0.038783967 0.34232074 0.7574931 0.5870077 0.07973069 0.19481057 0.44524097 0.7037706 0.23394829 0.8872666 0.31292546 0.49419212 0.6515351 0.96001244 0.757661 0.30836463 0.45896745 0.61464846 0.5306744 0.1973759 0.6726239 0.7617512 0.89268243 0.40406454 0.18917644 0.46216476 0.9592315 0.8836499 0.120380044 0.88171166 0.16685468 0.0099749565 0.77837104 0.9142971 0.59533936 0.83060515 0.9382536 0.38473904 0.5720752 0.0923897 0.47951984 0.78906745 0.21368235 0.27070147 0.20481867 0.634039 0.7906462 0.03149581 0.6037537 0.16486454 0.6337949 0.63599354 0.37994426 0.875326 0.5022932 0.5131019 0.7064835 0.89525354 0.26925635 0.40770024 0.5105541 0.8787377 0.8122851 0.14915699 0.5661896 0.41567653 0.86253583 0.37459815 0.82650363 0.30235136 0.70389533 0.3331719 0.5296713 0.70010036 0.043084323 0.08784783 0.9479032 0.0030584335 0.38550806 0.28456324 0.3521104 0.49137837 0.51327944 0.2981676 0.47424924 0.73200727 0.634908 0.92211485 0.733642 0.31947094 0.3174262 0.3092844 0.31535137 0.4692194 0.33636367 0.76783687 0.48218673 0.058509707 0.8254637 0.4104643 0.7625837 0.11600304 0.80792093 0.84764034 0.591494 0.09496701 0.13867772 0.63801014 0.24602097 0.5130968 0.74819916 0.3518833 0.34483004 0.14519775 0.70943934 0.16549945 0.24931103 0.8329101 0.36523682 0.9261066 0.3178342 0.874189 0.5285853 0.8739634 0.6097448 0.1532343 0.09832662 0.7317918 0.16936713 0.58455575 0.0725463 0.34033418 0.081994414 0.05057901 0.82859695 0.4814958 0.78997093 0.08397001 0.49729997 0.32691693 0.8619627 0.37041503 0.11210239 0.9209436 0.80322516 0.21577549 0.95772505 0.33973914 0.32169586 0.6334582 0.12036353 0.8757397 0.98095363 0.29907155 0.62645966 0.9292289 0.43128407 0.9003368 0.6869101 0.9289633 0.14624113 0.3669744 0.056056976 0.01021111 0.11044979 0.7550249 0.41316617 0.8082649 0.9297937 0.5631439 0.8187938 0.09409559 0.3219306 0.63605446 0.63870305 0.58645844 0.005045116 0.9906501 0.11913252 0.4238779 0.005495012 0.5637486 0.26354533 0.9501764 0.8729839 0.5662449 0.2156691 0.6978693 0.94222224 0.031453907 0.19485706 0.73466504 0.15186518 0.42640722 0.23241347 0.3880232 0.16903275 0.44661844 0.8356153 0.7843292 0.8016467 0.74180955 0.6703586 0.07446885 0.038995385 0.1572398 0.6798749 0.99531585 0.89484054 0.30501127 0.07924938 0.6544866 0.6632257 0.3148536 0.38134533 0.33267784 0.6992869 0.06526184 0.74273276 0.90453714 0.96592575 0.30533642 0.79159427 0.80429107 0.5288203 0.66142774 0.876708 0.85780436 0.6694377 0.9822218 0.14789546 0.9322929 0.4326231 0.6575933 0.95024824 0.7252839 0.88230616 0.90077686 0.6204153 0.8667819 0.2079544 0.9680625 0.08663666 0.8407884 0.011371553 0.72532463 0.22397739 0.90405416 0.21073562 0.7705481 0.9260319 0.9917926 0.0590415 0.93610096 0.35400993 0.33947474 0.66256315 0.90419596 0.10514581 0.47917718 0.74200606 0.10904759 0.39316183 0.74216026 0.37297755 0.9000383 0.22260755 0.83114326 0.59197176 0.4038741 0.6026808 0.07720852 0.45590085 0.40254855 0.6158364 0.044356465 0.61685735 0.09718239 0.78767335 0.5154681 0.75862294 0.49397504 0.9684169 0.63024473 0.43699908 0.23822021 0.63410264 0.70445925 0.5251986 0.50600237 0.11580366 0.049598098 0.11247724 0.40257144 0.77953184 0.8211796 0.91881293 0.7099636 0.06470287 0.8179248 0.27155524 0.7697807 0.8881825 0.5615386 0.38020062 0.40660697 0.6045667 0.82835376 0.5088776 0.96468824 0.6978376 0.6906921 0.15321583 0.32356322 0.63619596 0.86305153 0.73281276 0.07885754 0.22856122 0.96964324 0.7619441 1.2266636E-4 0.16953361 0.5422112 0.045706093 0.3738255 0.07553196 0.8126935 0.62077105 0.3860051 0.4062304 0.52651966 0.4838633 0.73389983 0.7578603 0.47614688 0.6470144 0.96999437 0.2897457 0.19777447 0.37037802 0.9750437 0.44611365 0.25641453 0.78035367 0.40703547 0.78109086 0.11645788 0.043342948 0.29914862 0.6775741 0.540702 0.63955534 0.38529503 0.09443253 0.62855124 0.06997985 0.49151647 0.119651616 0.11891335 0.4213887 0.050338864 0.20337176 0.51622176 0.64467794 0.98933953 0.14582378 0.9861885 0.8524618 0.79073817 0.24330968 0.17300367 0.38095552 0.032481372 0.60511684 0.42668164 0.604697 0.39428908 0.2421484 0.736421 0.072230875 0.32249403 0.89364475 0.66780084 0.42223656 0.2701621 0.8569806 0.94118804 0.18814647 0.62122107 0.2821908 0.37415653 0.5607427 0.21738416 0.7529837 0.8451714 0.71525836 0.95919067 0.4300788 0.60966873 0.5196063 0.1481719 0.54269814 0.65128493 0.16947466 0.076117516 0.89108264 0.08738971 0.17657638 0.828615 0.6446138 0.59188724 0.83125436 0.9290612 0.74917835 0.89476305 0.47035378 0.74763954 0.017207861 0.035217285 0.5093898 0.6662347 0.0775733 0.8193445 0.8713341 0.09906536 0.09923297 0.007324815 0.0593825 0.17078555 0.40752888 0.7161676 0.0023852587 0.7787505 0.3395381 0.67657566 0.21843088 0.8022987 0.73131454 0.6484942 0.066269934 0.68025887 0.12215638 0.11036688 0.45552313 0.36445504 0.13567317 0.15819925 0.31699526 0.24151951 0.25717252 0.8378519 0.06583536 0.9274917 0.310507 0.18274367 0.83619136 0.11451441 0.12746847 0.97481304 0.16114593 0.7894328 0.18998057 0.1287418 0.35350275 0.69223595 0.29199874 0.7805962 0.642304 0.30167377 0.59798586 0.068752825 0.5838913 0.12373966 0.9525936 0.97202927 0.5040873 0.0823673 0.9477018 0.44312876 0.8714238 0.09833825 0.7072151 0.14677894 0.4154511 0.5264166 0.9763274 0.46193516 0.701561 0.61405903 0.8716572 0.7863121 0.107227564 0.72561395 0.10735172 0.50594187 0.37326658 0.21366602 0.9271994 0.8933898 0.7477027 0.94632214 0.61172616 0.37536657 0.41431344 0.3682446 0.5514707 0.6694095</div></td>
		<td>o0 = or (and (nor (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2))) x4) (nand (or (or (nand (nand x5 (nand x5 x3)) (nand x3 (nor x5 x5))) (or (and x0 x2) (and x4 (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2)))))) (nand (nand x5 (nand x5 x3)) (nand x3 (nor x5 x5)))) (nor (not (nand (or (and x0 x2) (and x4 (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2))))) (and (nand x5 x3) (nand (nand x5 (nand x5 x3)) (nand x3 (nor x5 x5)))))) (and x5 (nor (and x0 x2) (nand x3 (nor x5 x5))))))) (and (nor (nand (nand (and (nand (nand x3 (nor x5 x5)) (nand (and x0 x2) (and x4 (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2)))))) (nand (and (nand x5 x3) (nand (nand x5 (nand x5 x3)) (nand x3 (nor x5 x5)))) (nand (nand (and x0 x2) (and x4 (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2))))) (not (nand (or (and x0 x2) (and x4 (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2))))) (and (nand x5 x3) (nand (nand x5 (nand x5 x3)) (nand x3 (nor x5 x5))))))))) (nand x5 x1)) (or (nand (or (and x0 x2) (and x4 (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2))))) (and (nand x5 x3) (nand (nand x5 (nand x5 x3)) (nand x3 (nor x5 x5))))) (nand (nand x3 (nor x5 x5)) (nand (and x0 x2) (and x4 (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2)))))))) (nor (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2))) x4)) (or (nand (nand x5 (nand x5 x3)) (nand x3 (nor x5 x5))) (or (and x0 x2) (and x4 (nor (nor x1 (or x0 x2)) (and x1 (or x0 x2)))))))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1</td>
	</tr>
	<tr valign="top">
		<td>7</td>
		<td>37528</td>
		<td class="g"><div class="genome">0.6579876 0.03996265 0.88051796 0.85262644 0.72988766 0.5076581 0.7678254 0.63303304 0.90513074 0.20588773 0.5005265 0.6451163 0.45548564 0.54075193 0.02330631 0.48414397 0.32714957 0.55542684 0.015930355 0.5425754 0.48608392 0.37472206 0.38883054 0.3376174 0.955843 0.42322958 0.059756935 0.22054118 0.592515 0.5172727 0.7405824 0.46535867 0.05709684 0.2353282 0.6708636 0.507159 0.8962633 0.7797917 0.8410972 0.7610129 0.9983146 0.9696231 0.19328415 0.6093106 0.06195414 0.30097318 0.9311002 0.15659314 0.6449597 0.81034195 0.068282664 0.8854913 0.6096147 0.36863297 0.8063162 0.5174173 0.98353505 0.25667232 0.8905823 0.8302395 0.022072434 0.07974458 0.9656953 0.7369895 0.07536632 0.95957464 0.21816081 0.2805949 0.57776314 0.4100142 0.23717225 0.3447519 0.5675395 0.5070413 0.90236306 0.7620365 0.37022132 0.09898543 0.03764671 0.9694791 0.6841003 0.9353788 0.61830354 0.9326235 0.02210033 0.31438142 0.43491864 0.80073017 0.34531337 0.5450879 0.9312972 0.015885353 0.8005933 0.95957816 0.38155657 0.2144689 0.19838506 0.40334898 0.7743453 0.45323336 0.83071434 0.2375524 0.51286244 0.97249967 0.85902935 0.2836051 0.57310766 0.65137863 0.065283656 0.5749748 0.63812846 0.3504758 0.6889939 0.94095814 0.08941251 0.92578906 0.6286176 0.6934329 0.58207905 0.6185106 0.00949657 0.5509556 0.22599846 0.85302895 0.648812 0.4533733 0.19333857 0.8446502 0.80384725 0.5484333 0.059284747 0.17204648 0.5259053 0.74481165 0.9050687 0.04071498 0.18250805 0.6055491 0.19391924 0.86447686 0.79636955 0.9505396 0.024709165 0.367316 0.66754246 0.6598275 0.6090382 0.15298092 0.5718204 0.8666365 0.56654733 0.81916314 0.73860896 0.7980433 0.49166703 0.74816716 0.51201946 0.44629472 0.90896624 0.9125104 0.21856588 0.90894264 0.7490292 0.9651407 0.6497408 0.921636 0.73022527 0.6933683 0.0065562725 0.92484045 0.8120671 0.54867196 0.21046007 0.8148791 0.8272041 0.051972747 0.36082584 0.41322643 0.31073922 0.79402566 0.86118096 0.065623224 0.9230042 0.07512289 0.55157936 0.17570847 0.54292357 0.26587456 0.053462625 0.67692536 0.79016733 0.11752367 0.071222365 0.5218951 0.34245336 0.8470311 0.95858276 0.50871193 0.6734594 0.979733 0.80177116 0.3117587 0.12767977 0.42045277 0.1402899 0.5285316 0.7652146 0.42011613 0.89145356 0.5369206 0.7550409 0.7236876 0.39991283 0.7381077 0.44686365 0.26747453 0.955801 0.50053227 0.7356429 0.34265685 0.6985347 0.0240075 0.44734704 0.7852667 0.99451894 0.45888758 0.006359935 0.44619405 0.34150028 0.46621656 0.33105338 0.40881914 0.5185496 0.46962088 0.67546105 0.9356276 0.7162989 0.36502886 0.5088723 0.10647166 0.096580625 0.008813322 0.18967938 0.29187793 0.42134678 0.6013006 0.58340716 0.46670187 0.76069826 0.1517613 0.16152269 0.26882225 0.5241875 0.101869285 0.9000093 0.8907357 0.1392206 0.28688943 0.30413383 0.40306324 0.7842779 0.78189015 0.8865103 0.2896918 0.3109637 0.037367344 0.11313319 0.7585863 0.20951748 0.71256715 0.80614954 0.47412658 0.47039574 0.46291226 0.33659595 0.4758672 0.11902565 0.41831917 0.24484193 0.90395236 0.10427278 0.9459057 0.103030145 0.8736229 0.15807849 0.6231454 0.37559938 0.52509946 0.55027837 0.44563633 0.9255772 0.8737951 0.7612624 0.80234575 0.4182536 0.7368496 0.15800434 0.32582682 0.03083539 0.8413413 0.27715534 0.5941972 0.5165649 0.45585215 0.47634226 0.7436939 0.7191891 0.05344826 0.014206767 0.3719753 0.11562008 0.45095217 0.33768368 0.047269583 0.7345376 0.20927548 0.3297602 0.3848061 0.19556332 0.54770863 0.009221554 0.60201925 0.14856994 0.92135113 0.23919451 0.23067945 0.4930026 0.5895581 0.3912316 0.073236704 0.50969595 0.19559103 0.18627846 0.17529225 0.34063536 0.11212641 0.77915466 0.9220743 0.78102505 0.9605053 0.24546695 0.29058135 0.11817932 0.6644871 0.7340043 0.5304779 0.30069804 0.688277 0.81797063 0.97089034 0.64171207 0.35124463 0.5644736 0.97238433 0.32755643 0.39867908 0.4265229 0.06049341 0.5056757 0.27278292 0.017924309 0.8167723 0.6278833 0.39215118 0.99535125 0.6518026 0.5288531 0.53631794 0.40819722 0.5237162 0.7408247 0.23321277 0.32886785 0.4678626 0.79652864 0.25762373 0.9979372 0.8300675 0.58735263 0.6482224 0.29058403 0.47356635 0.81277716 0.5133581 0.585116 0.6588772 0.84398985 0.7636352 0.36076188 0.31240922 0.15461534 0.35824102 0.45596397 0.2685483 0.5113759 0.4700657 0.19917965 0.94404984 0.92199665 0.31572628 0.8810959 0.26554376 0.5155048 0.45495158 0.5291399 0.7575373 0.18314582 0.61099106 0.23066765 0.9608821 0.732246 0.40397447 0.33970612 0.34761685 0.40932786 0.6901718 0.38212055 0.9548024 0.7135121 0.0717082 0.4778453 0.30280048 0.02560997 0.5068038 0.21607292 0.43862802 0.43459147 0.5605801 0.76705563 0.38775396 0.5907067 0.5784091 0.47325975 0.33675247 0.9017229 0.845521 0.82691294 0.96164083 0.6986469 0.8995719 0.63118804 0.5122073 0.0570727 0.34810877 0.8897417 0.363953 0.44945818 0.637343 0.8237234 0.43969083 0.30979425 0.7937014 0.75902355 0.01742667 0.56111825 0.66157186 0.67303914 0.009585023 0.95891285 0.4104489 0.22191882 0.87307674 0.81431776 0.1472925 0.23726988 0.7606145 0.2399944 0.20267689 0.6933798 0.24472982 0.42540205 0.15891564 0.72214395 0.5127041 0.7909089 0.72387826 0.73315924 0.48255336 0.074421704 0.04381162 0.30940086 0.12704897 0.024470031 0.42391032 0.7540671 0.35456038 0.7425154 0.07398355 0.9671838 0.045566678 0.6497934 0.09897345 0.27793843 0.30883366 0.8757336 0.7899546 0.60889286 0.12230557 0.49440813 0.62224394 0.23391569 0.77178705 0.7620565 0.7700859 0.96206784 0.056700826 0.1679663 0.7832761 0.62587243 0.36111814 0.08378929 0.5196542 0.59269667 0.33360034 0.10820031 0.7540561 0.35063297 0.79723233 0.5428677 0.6138266 0.92312056 0.6250407 0.9134426 0.921023 0.40418524 0.6316944 0.14384729 0.2162022 0.9884954 0.5395016 0.39938927 0.68737483 0.9304373 0.10511553 0.84970915 0.39066714 0.3503527 0.6680094 0.9949432 0.85996425 0.45735997 0.11535907 0.25047374 0.28163671 0.033030987 0.80970556 0.81210595 0.58715665 0.8701197 0.28884447 0.6090289 0.532694 0.98541105 0.33842355 0.9288074 0.9373296 0.16845751 0.75258017 0.23701555 0.23361492 0.078519225 0.3347817 0.004237354 0.96189386 0.5279856 0.66548777 0.6346898 0.21844172 0.95254415 0.7305803 0.24372065 0.9447346 0.5465382 0.09793401 0.962163 0.699228 0.4345562 0.7359343 0.11714733 0.7385827 0.9161027 0.50538766 0.03749633 0.8149288 0.6190924 0.06111145 0.45210153 0.59604436 0.9247291 0.093252 0.8490528 0.7897165 0.45654196 0.9032184 0.5930786 0.9076978 0.788359 0.8951769 0.5822974 0.67602724 0.6746623</div></td>
		<td>o0 = and (or (nor (nor (nor x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1))) (and (nand (or x5 x4) (nand x5 x4)) (and x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1))))) (and (nand x6 x0) (not (nor x0 x6)))) (nand (nand (and (nand x6 x0) (not (nor x0 x6))) (nor (nor x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1))) (and (nand (or x5 x4) (nand x5 x4)) (and x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1)))))) (or (nand (or x5 x4) (nand x5 x4)) (and x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1)))))) (or (and (not (and (not (and (nand x6 x0) (not (nor x0 x6)))) (nor x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1))))) (nand (and (nand x6 x0) (not (nor x0 x6))) (nor (nor x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1))) (and (nand (or x5 x4) (nand x5 x4)) (and x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1))))))) (or (nand (or x5 x4) (nand x5 x4)) (and x2 (or (nor (or (not x3) (nor x5 (nand x5 x4))) x1) (and (not x3) x1)))))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0</td>
	</tr>
	<tr valign="top">
		<td>8</td>
		<td>34833</td>
		<td class="g"><div class="genome">0.10954255 0.48224092 0.46681583 0.34252483 0.7664831 0.37740153 0.3736204 0.6628268 0.3301915 0.85412973 0.44401312 0.8685247 0.7458012 0.17548645 0.99042714 0.7117943 0.9409516 0.11081171 0.27047408 0.6106455 0.43604916 0.0066403747 0.25174975 0.40277714 0.95614535 0.45439214 0.8474216 0.26513177 0.2687077 0.6325888 0.80908525 0.06348789 0.68153805 0.31564844 0.30568784 0.939469 0.58927685 0.2694357 0.2476868 0.9880212 0.12291074 0.5656549 0.94987965 0.822102 0.91875905 0.24445283 0.31931782 0.7713882 0.15245932 0.8967692 0.01428473 0.5194638 0.70039254 0.4958142 0.70867276 0.46252573 0.60973924 0.056416333 0.7738275 0.74313766 0.29432392 0.5079751 0.71945345 0.112333596 0.3396405 0.6590314 0.65754503 0.70126903 0.0056837797 0.31720513 0.6111728 0.8854105 0.6342725 0.96159065 0.7511039 0.88219464 0.4793657 0.6175858 0.5268435 0.6517241 0.18234223 0.44755107 0.013417304 0.53122586 0.518594 0.9724091 0.24055696 0.3934307 0.14057404 0.22651011 0.0028879046 0.50193506 0.88916534 0.37689978 0.18223 0.34150302 0.32771248 0.39220017 0.80484825 0.95072234 0.7407162 0.19677502 0.06439608 0.55386764 0.88316333 0.17878312 0.24612719 0.028111935 0.1780051 0.02940774 0.7162664 0.39581233 0.71183634 0.7690542 0.96127826 0.6958442 0.014342248 0.10345441 0.08438504 0.33720613 0.23690814 0.68073684 0.32229733 0.2244333 0.82691216 0.9073935 0.9043009 0.3128428 0.16155422 0.74951744 0.7385935 0.3741752 0.9846541 0.8787905 0.29030097 0.15879875 0.6455603 0.44896913 0.918369 0.3155378 0.42321986 0.87007356 0.30267566 0.71593344 0.25752795 0.5501068 0.81474453 0.40886867 0.9667709 0.8552713 0.7315002 0.27981484 0.8147591 0.46856242 0.07956147 0.4986182 0.8695726 0.80410314 0.8727911 0.8194493 0.14477056 0.10113394 0.31258363 0.6258156 0.5286001 0.69334763 0.24850297 0.24117059 0.57844234 0.86067766 0.6572551 0.6246035 0.63575095 0.82980794 0.6054082 0.81566626 0.96825725 0.38654763 0.7226379 0.95792204 0.83912313 0.47565585 0.98864686 0.7655661 0.2897808 0.14199632 0.59641665 0.8491058 0.41344708 0.42313772 0.0823015 0.89104784 0.5517403 0.9003161 0.008063734 0.20972294 0.82744867 0.9990688 0.89507097 0.9903889 0.5168143 0.6194119 0.12456989 0.017019093 0.82436687 0.6623537 0.25400275 0.8461796 0.6524071 0.48711032 0.5451274 0.8212394 0.64321303 0.90789914 0.69225323 0.85579205 0.9393996 0.750569 0.39574063 0.24323791 0.1549868 0.32389033 0.29658705 0.2815042 0.66269857 0.72547096 0.14186096 0.561776 0.4649713 0.0059614778 0.9983655 0.088234484 0.054117322 0.72156036 0.54514515 0.8228873 0.6930626 0.7315932 0.7905756 0.9153494 0.61294174 0.9086008 0.6219134 0.96233636 0.9955698 0.54403406 0.58615816 0.26880282 0.9032173 0.98142505 0.19522983 0.60602075 0.13460273 0.2569328 0.8763514 0.02094853 0.9215094 0.8763864 0.20529157 0.079610586 0.13890123 0.54364306 0.41541034 0.6536231 0.20576489 0.34276205 0.0043458343 0.10660678 0.034794807 0.9280074 0.5771755 0.44168872 0.36161005 0.33667696 0.10317731 0.47218657 0.6267034 0.7452538 0.7917761 0.98651683 0.2488082 0.6823978 0.34392107 0.7822034 0.42349195 0.36584067 0.91268516 0.38255018 0.90176016 0.5286735 0.3698477 0.022237122 0.800251 0.49772865 0.20361686 0.42272496 0.48482794 0.11563182 0.5742759 0.8642011 0.36520797 0.4752155 0.7822823 0.0039716363 0.27285248 0.21171457 0.2561407 0.7956691 0.36276245 0.36574852 0.5451202 0.2817096 0.6325388 0.33204788 0.84767973 0.12356299 0.33783764 0.23640645 0.7522253 0.6589549 0.40128 0.628395 0.73270315 0.39314026 0.66973907 0.9476923 0.16184562 0.8619694 0.2968318 0.3641522 0.065309644 0.8150227 0.052948117 0.04327917 0.34589547 0.8337875 0.63453 0.8953589 0.06703299 0.105805695 0.45770746 0.9320371 0.6984266 0.7120741 0.69343466 0.041364193 0.9393133 0.4239313 0.9789863 0.285663 0.18121415 0.2979439 0.14144021 0.56113523 0.6021108 0.7871279 0.92407227 0.48365885 0.23449314 0.9025521 0.38960433 0.098279536 0.22151309 0.23439622 0.8491946 0.7572881 0.62642884 0.6585805 0.28385615 0.04039347 0.34572238 0.8818669 0.6343261 0.27002794 0.5215447 0.647482 0.26568103 0.31312966 0.66916037 0.4720838 0.81991976 0.88098866 0.22673565 0.42397708 0.9646649 0.6957008 0.8422461 0.6196047 0.064116 0.1380353 0.39706635 0.11413932 0.37339783 0.35104555 0.22833502 0.46320796 0.009612322 0.57188797 0.79323965 0.631252 0.81276786 0.53129166 0.2590289 0.98892266 0.39248097 0.8583512 0.5794198 0.896734 0.9543241 0.4434995 0.40398765 0.30920523 0.92007995 0.59062976 0.26599026 0.3466562 0.81067574 0.3683946 0.7009427 0.16813248 0.5591693 0.23844123 0.70639515 0.035000265 0.23499697 0.008642316 0.66802514 0.25068718 0.67643416 0.017850757 0.19302374 0.5826627 0.07538992 0.70068824 0.8604875 0.5913589 0.4234512 0.08726549 0.72905517 0.30883086 0.6457734 0.110334754 0.8377233 0.90659565 0.32572466 0.4076662 0.8542062 0.58683944 0.28653038 0.81223416 0.7037227 0.7596105 0.7885061 0.26792675 0.3943776 0.7822841 0.20967913 0.75508016 0.5177328 0.1252296 0.3106063 0.45757955 0.5568526 0.114549994 0.20336002 0.71544844 0.53605366 0.86030203 0.3917358 0.37731963 0.094798684 0.87986565 0.9440336 0.44641912 0.9995687 0.21344388 0.046275496 0.601736 0.8985279 0.5361239 0.37407964 0.5585397 0.46098363 0.8282116 0.6556211 0.93216044 0.84403086 0.8803353 0.72757846 0.7308006 0.043788075 0.856904 0.91330916 0.8693861 0.9237943 0.570728 0.28841668 0.8486402 0.17530656 0.50274783 0.058906734 0.33841205 0.6818409 0.57145214 0.47173506 0.94259906 0.24002528 0.3610618 0.53745717 0.4062388 0.83532476 0.19526458 0.3248517 0.8842706 0.16347235 0.083661854 0.9808781 0.8207927 0.95522976 0.8275869 0.42427438 0.4731316 0.59316784 0.070397735 0.90297747 0.4251653 0.5461141 0.8246033 0.5039178 0.6087761 0.45931327 0.7001393 0.6596857 0.017287374 0.24968833 0.72244304 0.08664662 0.1744141 0.2914899 0.96827644 0.25923532 0.92849296 0.75575316 0.6773443 0.9580129 0.18203342 0.041708052 0.9419405 0.52704203 0.3831728 0.47817498 0.05933702 0.93391234 0.20644987 0.56950724 0.091234386 0.81479144 0.8448396 0.086704135 0.7659202 0.70219433 0.7879589 0.22417313 0.8639706 0.847691 0.75366 0.43996245 0.030487478 0.7798379 0.49002713 0.4766912 0.24799007 0.9393018 0.25539446 0.17171884 0.38730407 0.7249187 0.50745827 0.62194926 0.1778549 0.7707897 0.81291974 0.66987634 0.5599216 0.8564364 0.8170526 0.64850944 0.027316391 0.61211526 0.1427232 0.6047926 0.8966656 0.79343814 0.9852586 0.34167624 0.049875557 0.74276614 0.5298229 0.8870256 0.8186128 0.22457838 0.026355565 0.28091937 0.1859724 0.510603 0.72754234 0.51117766 0.5737409 0.42892414 0.24011976 0.2718141 0.26846892 0.9554921 0.2841161 0.819756 0.8646598 0.51415217 0.00578779 0.8315096 0.4337715 0.7009037 0.28947592 0.99099743 0.058905005 0.46863765 0.64059615 0.2453875 0.9218658 0.5718415 0.58759534 0.893568 0.1743474 0.2341758 0.071911335 0.5654293 0.5684121 0.06822711 0.56993335 0.943576 0.33536732 0.40105402 0.6878951 0.6063983 0.76969415 0.23767215 0.09690201 0.92680943 0.73222476 0.14620078 0.51855975 0.20079458 0.35583043 0.12174213 0.3248369 0.5781426 0.18441808 0.4394188 0.76759034 0.7869407 0.32214588 0.47412384 0.48064566 0.52748454 0.6832894 0.24962306 0.97807264 0.25707877 0.28779477 0.9556813 0.20742244 0.25159103 0.6270525 0.9553996 0.2555241 0.60045975 0.50518715 0.4390654 0.60948104 0.4007995 0.92428243 0.6740494 0.24432534 0.9632705 0.1489234 0.2055341 0.6521971 0.17043889 0.8593704 0.016254902 0.4719481 0.039547324 0.39807475 0.5989116 0.5857473 0.6142391 0.154208 0.54125696 0.66241235 0.14461905 0.54879814 0.68790185 0.7986781 0.6437818 0.39811802 0.17636746 0.58711284 0.84486324 0.057834387 0.59784716 0.24771804 0.7671458 0.97984916 0.28499454 0.9765206 0.5719054 0.48492563 0.9472242 0.58743304 0.32652724 0.7159441 0.63272744 0.874241 0.14369673 0.17561597 0.47955394 0.7822104 0.18932825 0.9841286 0.4416132 0.33758533 0.9379171 0.2808053 0.38577455 0.9866472 0.7969603 0.40122443 0.90850765 0.08770722 0.2101568 0.9581561 0.25422168 0.8194785 0.09106982 0.60228276 0.84254146 0.58696824 0.95834184 0.8610069 0.72192794 0.8631461 0.79336756 0.85421646 0.59689397 0.06506997 0.978193 0.40468055 0.8825238 0.49991828 0.67066103 0.462093 0.2661155 0.024219334 0.104124606 0.3209191 0.8058778 0.31658012 0.31733888 0.88887197 0.92306334 0.4410941 0.4697855 0.50165015 0.025622427 0.9605954 0.8337764 0.1152432 0.39784974 0.84722245 0.9728087 0.38299006 0.98045427 0.46272177 0.91931874 0.1472103 0.9631681 0.7013166 0.16681981 0.6719066 0.1312837 0.20126116 0.79184645 0.87796074 0.7104898 0.071406305 0.20350832 0.686248 0.85636413 0.8193501 0.3466192 0.55928206 0.84645087 0.67285407 0.33669454 0.68307495 0.9716988 0.44230264 0.17258954 0.1342411 0.82576525 0.6075474 0.9923787 0.31846827 0.026298404 0.12579948 0.5402063 0.3918506 0.24950165 0.15559459 0.046691477 0.11940527 0.7235497 0.6424677 0.2797801 0.16788453 0.04731375 0.51049817 0.18417567 0.38518977 0.96309865 0.07861364 0.34658217 0.35523003 0.9471404 0.35163164 0.7322476 0.5326964 0.8645627 0.7173221 0.09953803 0.19711 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		<td>o0 = and (or (and (and (not (nor (not (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)))) (and (or (or x6 x3) (not (not (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)))))) (nor (nor (nand (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (or (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (not (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)))))) (nand (nand (or x4 (or x6 x3)) (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5))) (or (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (not (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1))))))) (and (nand (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (not (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5)))) (and x3 x6))) (or (nand x1 (nor x2 (nand x4 (or x6 x3)))) (and (not x5) (not x5)))) (or (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (not (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5)))) (and x3 x6))) (or (nand x1 (nor x2 (nand x4 (or x6 x3)))) (and (not x5) (not x5))))))))) (nand (nand (or x4 (or x6 x3)) (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5))) (and (or x5 (or x4 (or x6 x3))) (nand (and x3 x6) (not x5))))) (or (and (nor (nand (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (or (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (not (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)))))) (nand (nand (or x4 (or x6 x3)) (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5))) (or (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (not (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1))))))) (or x6 x3)) (or (and (nand (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (not (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5)))) (and x3 x6))) (or (nand x1 (nor x2 (nand x4 (or x6 x3)))) (and (not x5) (not x5)))) (or (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (not (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5)))) (and x3 x6))) (or (nand x1 (nor x2 (nand x4 (or x6 x3)))) (and (not x5) (not x5))))) (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)))))) (nor (nand (or (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (nand (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (not (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5)))) (and x3 x6))) (not (not (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)))))) (and (or x5 (or x4 (or x6 x3))) (nand (and x3 x6) (not x5)))) (nand (nand (or x4 (or x6 x3)) (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5))) (or (or (nor (nor (nand x2 (nand x4 (or x6 x3))) x0) (and (nand x2 (nand x4 (or x6 x3))) x0)) (and x3 x6)) (not (nand (nand x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)) (or x7 (nor (nor x2 (nand x4 (or x6 x3))) x1)))))))) (or (or x7 (or x4 (or x6 x3))) (nand (or x4 (or x6 x3)) (nand (nand x1 (nor x2 (nand x4 (or x6 x3)))) (not x5))))</td>
		<td>1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1</td>
	</tr>
</table>


<a name="ref"></a><h1>References</h1>
<p><i>You can find many papers related to CGP on <a href="http://www.cartesiangp.co.uk/publications.html">Julian Miller's web site</a>.</i>
<p><sup><a name="r1"></a>[1]</sup> Julian F. Miller, Peter Thomson: Cartesian Genetic Programming. EuroGP 2000: 121-132</p>
<p><sup><a name="r2"></a>[2]</sup> J. Clegg, J. A. Walker and J. F. Miller, A New Crossover Technique for Cartesian Genetic Programming, Proceedings of the 2007 Genetic and Evolutionary Computation Conference (GECCO 2007), 2007.</p>
<p><sup><a name="r3"></a>[3]</sup> Julian F. Miller: What bloat? Cartesian Genetic Programming on boolean problems, 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers, 2001. </p>
<p><sup><a name="r4"></a>[4]</sup> J. F. Miller and S. L. Smith, Redundancy and Computational Efficiency in Cartesian Genetic Programming, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 2, pp. 167-174, 2006. </p>
<p><sup><a name="r5"></a>[5]</sup> Michalski,R.S., Mozetic,I., Hong,J., &amp; Lavrac,N. (1986). The  Multi-Purpose Incremental Learning System AQ15 and its Testing  Application to Three Medical Domains.  In Proceedings of the  Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann.</p>
<p><sup><a name="r6"></a>[6]</sup> Clark,P. &amp; Niblett,T. (1987). Induction in Noisy Domains.  In Progress in Machine Learning (from the Proceedings of the 2nd European Working Session on Learning), 11-30, Bled, Yugoslavia: Sigma Press.</p>
<p><sup><a name="r7"></a>[7]</sup> Tan, M., &amp; Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains.  Proceedings of the Fifth International Conference on Machine Learning, 121-134, Ann Arbor, MI.</p>
<p><sup><a name="r8"></a>[8]</sup> Cestnik,G., Konenenko,I, &amp; Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users.  In I.Bratko &amp; N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.</p>
<p><sup><a name="r9"></a>[9]</sup> Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press; Koza, J.R. (1994).</p>




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Author: <a href="http://oranchak.com">David Oranchak</a>  <a href="mailto:doranchak@gmail.com">(email)</a>

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